Climate Data Prep
Brandie Quarles
2023-10-18
To Do:
Decide between elevation classifications vs. some climate metric to define growth seasons
- Categorical Growth Seasons:
Low: Oct-July
Mid: Nov-Aug
High: April-Sept
- Based on climate metrics: no snowpack and/or significant rain/lower
CWD
- Some pops grow all all year per the criteria below, is that possible? Also, some have really short growth season (1 or 2 months), is that possible?
- John and Sam have code for daily data, with “first day to x event” - Ask Sam if she’s calculated this for STTO pops
- K means clustering to see if it splits into the 3 groups (Can give it the 30 year vector. Probably need to turn off scaling)
- Categorical Growth Seasons:
PCAs for climate during the growth season
- Color monthly PCA by month relative to growth season?
- Make PCAs for historical climate too
- Bioclim vars only PCA okay? or combine with other Flint variables? Hard to combine b/c Flint data is for each month within a year and Bioclim variables are yearly summaries. Could do one with just Flint with all months in each year included and one with Flint growth season yearly averages combined with Bioclim variables
Relevant Libraries and Functions
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## steplibrary(naniar) #replaces values with NA
library(QBMS) #for function calc_biovars to calculate bioclim variables
sem <- function(x, na.rm=FALSE) {
sd(x,na.rm=na.rm)/sqrt(length(na.omit(x)))
} #standard error function
get_legend<-function(myggplot){
tmp <- ggplot_gtable(ggplot_build(myggplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)
} #legend function for grid_arrange
elev_three_palette <- c("#0043F0", "#C9727F", "#F5A540") #colors from Gremer et al 2019
elev_order <- c("High", "Mid", "Low")
#For scree plots
#library("devtools") #The package devtools is required for the installation as factoextra is hosted on github.
#1install_github("kassambara/factoextra")
library("factoextra")## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBaLoad the pop and location data
#pop info
pops_common_garden <- read_csv("../input/UCD_Data/Pops_for_2022_UCD.csv") #pops included in common garden ## Rows: 23 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): parent.pop, elevation.group, collection.priority., on.climate.PCA.,...
## dbl (5): phylogroup, maternal.families, approx.number.seeds, UCD.seed.year, ...
## lgl (1): JGI.DNA
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.summary(pops_common_garden)## parent.pop phylogroup elevation.group maternal.families
## Length:23 Min. :1.000 Length:23 Min. : 3.0
## Class :character 1st Qu.:2.000 Class :character 1st Qu.:16.5
## Mode :character Median :4.000 Mode :character Median :23.0
## Mean :4.435 Mean :22.7
## 3rd Qu.:7.000 3rd Qu.:29.5
## Max. :9.000 Max. :44.0
## approx.number.seeds UCD.seed.year proposed.WL2.seed.year collection.priority.
## Min. : 100.0 Min. :2014 Min. :2014 Length:23
## 1st Qu.: 500.0 1st Qu.:2018 1st Qu.:2020 Class :character
## Median : 500.0 Median :2020 Median :2021 Mode :character
## Mean : 608.7 Mean :2020 Mean :2020
## 3rd Qu.:1000.0 3rd Qu.:2021 3rd Qu.:2021
## Max. :1000.0 Max. :2021 Max. :2022
## on.climate.PCA. JGI.DNA notes
## Length:23 Mode:logical Length:23
## Class :character NA's:23 Class :character
## Mode :character Mode :character
##
##
## names(pops_common_garden)## [1] "parent.pop" "phylogroup" "elevation.group"
## [4] "maternal.families" "approx.number.seeds" "UCD.seed.year"
## [7] "proposed.WL2.seed.year" "collection.priority." "on.climate.PCA."
## [10] "JGI.DNA" "notes"pops_common_garden_nonotes <- pops_common_garden %>% select(parent.pop:elevation.group, UCD.seed.year)
pops_common_garden_nonotes## # A tibble: 23 × 4
## parent.pop phylogroup elevation.group UCD.seed.year
## <chr> <dbl> <chr> <dbl>
## 1 BH 4 low 2021
## 2 CC 7 low 2018
## 3 CP2 2 high 2019
## 4 CP3 2 high 2018
## 5 DPR 5 mid 2020
## 6 FR 7 mid 2019
## 7 IH 5 low 2021
## 8 LV1 9 high 2018
## 9 LV3 9 high 2018
## 10 LVTR1 9 high 2020
## # ℹ 13 more rowspops_common_garden_nonotes$elevation.group <- str_to_title(pops_common_garden$elevation.group)
#extra location info
pop_loc <- read_csv("../input/Strep_tort_locs.csv")## Rows: 54 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Species epithet, Species Code, Site, Site code, Lat, Long
## dbl (1): Elevation (m)
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.names(pop_loc)## [1] "Species epithet" "Species Code" "Site" "Site code"
## [5] "Lat" "Long" "Elevation (m)"head(pop_loc)## # A tibble: 6 × 7
## `Species epithet` `Species Code` Site `Site code` Lat Long `Elevation (m)`
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 Streptanthus tor… STTO Ben … BH 37.4… -119… 511.
## 2 Streptanthus tor… STTO Bidw… BB 39.5… -121… 283.
## 3 Streptanthus tor… STTO Cany… CC 39.5… -121… 313
## 4 Streptanthus tor… STTO Cars… CP1 38.6… -120… 2422.
## 5 Streptanthus tor… STTO Cars… CP2 38.6… -120… 2244.
## 6 Streptanthus tor… STTO Cars… CP3 38.7… -120… 2266.unique(pop_loc$`Site code`)## [1] "BH" "BB" "CC" "CP1" "CP2" "CP3" "DP" "DPR"
## [9] "FR" NA "HH" "IH" "KC1" "KC2" "KC3" "LV1"
## [17] "LV2" "LV3" "LVTR1" "LVTR2" "LVTR3" "SQ1" "SQ2" "SQ3"
## [25] "SHA" "SC" "TM1" "TM2" "WR" "WV" "WL1" "WL2"
## [33] "WL3" "WL4" "YOSE1" "YOSE10" "YOSE11" "YOSE12" "YOSE13" "YOSE2"
## [41] "YOSE3" "YOSE4" "YOSE5" "YOSE6" "YOSE7" "YOSE8" "YOSE9"unique(pops_common_garden_nonotes$parent.pop)## [1] "BH" "CC" "CP2" "CP3" "DPR" "FR" "IH" "LV1" "LV3"
## [10] "LVTR1" "SC" "SQ1" "SQ2" "SQ3" "TM2" "WL1" "WL2" "WR"
## [19] "WV" "YO11" "YO4" "YO7" "YO8"#need to change YOSE to YO
pop_loc_yo <- pop_loc %>% mutate(parent.pop = str_replace(`Site code`, "YOSE(\\d+)", "YO\\1")) %>% select(Lat, Long, elev_m=`Elevation (m)`, parent.pop)
unique(pop_loc_yo$parent.pop)## [1] "BH" "BB" "CC" "CP1" "CP2" "CP3" "DP" "DPR" "FR"
## [10] NA "HH" "IH" "KC1" "KC2" "KC3" "LV1" "LV2" "LV3"
## [19] "LVTR1" "LVTR2" "LVTR3" "SQ1" "SQ2" "SQ3" "SHA" "SC" "TM1"
## [28] "TM2" "WR" "WV" "WL1" "WL2" "WL3" "WL4" "YO1" "YO10"
## [37] "YO11" "YO12" "YO13" "YO2" "YO3" "YO4" "YO5" "YO6" "YO7"
## [46] "YO8" "YO9"#merge in location info
pop_elev <- left_join(pops_common_garden_nonotes, pop_loc_yo)## Joining with `by = join_by(parent.pop)`head(pop_elev)## # A tibble: 6 × 7
## parent.pop phylogroup elevation.group UCD.seed.year Lat Long elev_m
## <chr> <dbl> <chr> <dbl> <chr> <chr> <dbl>
## 1 BH 4 Low 2021 37.40985 -119.96458 511.
## 2 CC 7 Low 2018 39.58597 -121.43311 313
## 3 CP2 2 High 2019 38.66169 -120.13065 2244.
## 4 CP3 2 High 2018 38.70649 -120.08797 2266.
## 5 DPR 5 Mid 2020 39.22846 -120.81518 1019.
## 6 FR 7 Mid 2019 40.01362 -121.18498 787Map populations
names(pop_elev)## [1] "parent.pop" "phylogroup" "elevation.group" "UCD.seed.year"
## [5] "Lat" "Long" "elev_m"sapply(pop_elev, class) #lat and long are characters, need to be numeric ## parent.pop phylogroup elevation.group UCD.seed.year Lat
## "character" "numeric" "character" "numeric" "character"
## Long elev_m
## "character" "numeric"pop_elev <- pop_elev %>% mutate_at(c("Lat", "Long"), as.double)
states <- map_data("state") %>% filter(region == "california")
#without labels
ggplot() +
geom_polygon(data = states, aes(x = long, y = lat, group = group), fill = "gray") +
coord_quickmap(xlim = c(-125, -114), ylim = c(35, 43))+
geom_point(data = pop_elev,
aes(x = Long, y = Lat, color=elev_m),
size = 4) +
labs(x="Long", y="Lat", color="Elevation (m)") +
scale_colour_gradient(low = "#F5A540", high = "#0043F0") +
theme_classic() +
theme(text=element_text(size=25))
#ggsave("../output/Pop_Map_NoLabels.png", width = 6, height = 4, units = "in")
#with labels
ggplot() +
geom_polygon(data = states, aes(x = long, y = lat, group = group), fill = "gray") +
coord_quickmap(xlim = c(-125, -114), ylim = c(35, 43))+
geom_point(data = pop_elev,
aes(x = Long, y = Lat, color=elev_m),
size = 4) +
geom_text_repel(data = pop_elev,
aes(x = Long, y = Lat,
label = `parent.pop`),
min.segment.length = 0,
max.overlaps = 100,
# label.padding = 1,
# point.padding = 0.5,
size = 4) +
labs(x="Long", y="Lat", color="Elevation (m)") +
scale_colour_gradient(low = "#F5A540", high = "#0043F0") +
theme_classic() +
theme(text=element_text(size=25))
#ggsave("../output/Pop_Map_Labels.png", width = 12, height = 8, units = "in")Elevation group changes
names(pop_elev)
unique(pop_elev$parent.pop)
xtabs(~parent.pop+elevation.group, data = pop_elev)
pop_elev$elevation.group <- factor(pop_elev$elevation.group, levels=elev_order)
pop_elev %>% ggplot(aes(x=fct_reorder(parent.pop, elev_m), y=elev_m, fill=elevation.group)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_manual(values=elev_three_palette) +
labs(title="New Elevation Classifications", x="Parent Populations", y= "Elevation (m)") +
theme_classic() +
theme(text=element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Elevation_Class_New.png", width = 12, height = 6, units = "in")Flint Climate Data (1895-2022)
- aet (mm) = actual evapotranspiration (water transpired by plants, if water not limited)
- cwd (mm) = climate water deficit (estimate of drought) = pet-aet
- pck (mm) = snowpack (prior month pck + snow - lost snow)
- pet (mm) = potential evapotranspiration (total amount of water that can evaporate from the ground or be transpired by plants)
- ppt (mm) = precipitation
- rch (mm) = recharge (water that penetrates below the root zone)
- run (mm) = runoff (water that becomes streamflow)
- str (mm) = soil water storage (avg amount of water stored in the soil annually)
- all above are totals or sums per month
- tmn (deg C) = min air temp (for a given month)
- tmx (deg C) = max air temp (for a given month) 127 years
Notes
Did some lit review to see what variables other people have focused on or found to be significant (CWD, temp, precip, AET)
Temperature likely highly correlated within pops w/in years
Take all variables (including avg temp) for the 3 months when we think germination is occurring (Low: Oct-Jan, Mid: Nov-Jan, High: April-June)?
Decided to use growth season rather than germ season: Low: Oct-July, Mid: Nov-Aug, High: April-Sept
Load the climate data
climate_data <- read_csv("../input/Dimensions_All_1895-2022.csv")## Rows: 225552 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): pop
## dbl (12): year, month, aet, cwd, pck, pet, ppt, rch, run, str, tmn, tmx
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(climate_data)## # A tibble: 6 × 13
## pop year month aet cwd pck pet ppt rch run str tmn tmx
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CAAM-… 1895 10 25.8 62.8 0 88.6 6.78 0 0 108. 9.76 21.9
## 2 CAAM-… 1895 10 13.7 80.0 0 93.7 6.76 0 0 95.1 9.91 22.1
## 3 CAAM-… 1895 10 9.99 92.8 0 103. 6.59 0 0 129. 9.37 23.5
## 4 CAAM-… 1895 10 10.6 88.1 0 98.7 11.1 0 0 338. 9.30 23.1
## 5 CAAM-… 1895 10 8.85 90.6 0 99.4 6.29 0 0 103. 6.86 19.1
## 6 CAAM-… 1895 10 11.4 65.9 0 77.3 7.06 0 0 193. 4.45 15.9names(climate_data)## [1] "pop" "year" "month" "aet" "cwd" "pck" "pet" "ppt" "rch"
## [10] "run" "str" "tmn" "tmx"unique(climate_data$pop)## [1] "CAAM-CC" "CAAM-GB" "CAAM-Oaks" "CAAM-FMR" "CAAM-IHC"
## [6] "CAAM-MB" "CAAM-OCT" "CAAN-BCB" "CAAN1" "CAAN2"
## [11] "CAAN-BCR" "CAAN-BCT" "CAAN-CV3" "CAAN-CCMA" "CAAN-GH"
## [16] "CAAN-UC" "CAAN-SLSR" "CACO1" "CACO2" "CACO3"
## [21] "CACO-GSE" "CACO-OC" "CACO-BC" "CACO-Davis" "CACO-Oaks"
## [26] "CACO-BCR" "CACO-ODR" "CACO-SC" "CAIN2" "CAIN3"
## [31] "CAIN4" "CAIN-395" "CAIN-S-395" "CAIN-BCC" "CAIN-BCR"
## [36] "CAIN-KJ" "CAIN-MR" "CAIN-SMR" "STBR1" "STBR2"
## [41] "STBR3" "STBR-BCG" "STBR-HC" "STBR-LM" "STBR-MPCH"
## [46] "STBR-MCH" "STBR-MNJ" "STBR-WR" "STBR-M" "STDI"
## [51] "STDI-HL" "STDI-TM" "STDR2" "STDR-BR" "STDR-45"
## [56] "STDR-P1" "STDR-P2" "STGL1" "STGL2" "STGL3"
## [61] "STGL-BCR" "STGL-MH" "STGL-AQSM" "STGL-CR" "STGL-CHM"
## [66] "STGL-KBR" "STGL-MM" "STGL-TFA" "STGL-MRH" "STGL-MHO"
## [71] "STGL-MMSP" "STGL-MTSP" "STGL-SG" "STGL-SR" "STIN"
## [76] "STIN-JM" "STIN-TM" "STPO1" "STPO2" "STPO3"
## [81] "STPO-LS" "STPO-B" "STPO-F" "STPO-IHR" "STPO-P"
## [86] "STPO-PU" "STPO-R" "STPO-RH" "STPO-S" "STPO-YH"
## [91] "BH" "BB" "BR" "CC" "CP1"
## [96] "CP2" "CP3" "CL" "DP" "DPR"
## [101] "FR" "GM" "HH" "HM" "IH"
## [106] "JB" "KC1" "KC2" "KC3" "LV1"
## [111] "LV2" "LV3" "LVTR1" "LVTR2" "LVTR3"
## [116] "MSH" "PL" "RHC" "RB" "RG"
## [121] "SQ1" "SQ2" "SQ3" "SH" "SO"
## [126] "SC" "TM1" "TM2" "TFC" "WR"
## [131] "WV" "WL1" "WL2" "WL3" "WL4"
## [136] "YOSE1" "YOSE10" "YOSE11" "YOSE12" "YOSE13"
## [141] "YOSE2" "YOSE3" "YOSE4" "YOSE5" "YOSE6"
## [146] "YOSE7" "YOSE8" "YOSE9"climate_data$year = as.character(climate_data$year)
head(climate_data)## # A tibble: 6 × 13
## pop year month aet cwd pck pet ppt rch run str tmn tmx
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CAAM-… 1895 10 25.8 62.8 0 88.6 6.78 0 0 108. 9.76 21.9
## 2 CAAM-… 1895 10 13.7 80.0 0 93.7 6.76 0 0 95.1 9.91 22.1
## 3 CAAM-… 1895 10 9.99 92.8 0 103. 6.59 0 0 129. 9.37 23.5
## 4 CAAM-… 1895 10 10.6 88.1 0 98.7 11.1 0 0 338. 9.30 23.1
## 5 CAAM-… 1895 10 8.85 90.6 0 99.4 6.29 0 0 103. 6.86 19.1
## 6 CAAM-… 1895 10 11.4 65.9 0 77.3 7.06 0 0 193. 4.45 15.9climate_data_yo <- climate_data %>% mutate(pop = str_replace(pop, "YOSE(\\d+)", "YO\\1")) #changing YOSE to YO
#`(\\d+)`: This part contains a regular expression within parentheses:
#`\\d`: Matches a digit (equivalent to `[0-9]`).
#The double backslash `\\` is used to escape the `\` #character in R strings, so `\\d` is interpreted as `\d` in the regex, denoting a digit.
#`+`: This qualifier means "one or more of the preceding element," so `\\d+` will match one or more digits.
#`()`: Parentheses are used to define a capturing group, which means that the part of the regex within the parentheses `(\\d+)` is captured to be used in back-references or for extracting matched parts.
#\\1 references the first capturing group
unique(climate_data_yo$pop)## [1] "CAAM-CC" "CAAM-GB" "CAAM-Oaks" "CAAM-FMR" "CAAM-IHC"
## [6] "CAAM-MB" "CAAM-OCT" "CAAN-BCB" "CAAN1" "CAAN2"
## [11] "CAAN-BCR" "CAAN-BCT" "CAAN-CV3" "CAAN-CCMA" "CAAN-GH"
## [16] "CAAN-UC" "CAAN-SLSR" "CACO1" "CACO2" "CACO3"
## [21] "CACO-GSE" "CACO-OC" "CACO-BC" "CACO-Davis" "CACO-Oaks"
## [26] "CACO-BCR" "CACO-ODR" "CACO-SC" "CAIN2" "CAIN3"
## [31] "CAIN4" "CAIN-395" "CAIN-S-395" "CAIN-BCC" "CAIN-BCR"
## [36] "CAIN-KJ" "CAIN-MR" "CAIN-SMR" "STBR1" "STBR2"
## [41] "STBR3" "STBR-BCG" "STBR-HC" "STBR-LM" "STBR-MPCH"
## [46] "STBR-MCH" "STBR-MNJ" "STBR-WR" "STBR-M" "STDI"
## [51] "STDI-HL" "STDI-TM" "STDR2" "STDR-BR" "STDR-45"
## [56] "STDR-P1" "STDR-P2" "STGL1" "STGL2" "STGL3"
## [61] "STGL-BCR" "STGL-MH" "STGL-AQSM" "STGL-CR" "STGL-CHM"
## [66] "STGL-KBR" "STGL-MM" "STGL-TFA" "STGL-MRH" "STGL-MHO"
## [71] "STGL-MMSP" "STGL-MTSP" "STGL-SG" "STGL-SR" "STIN"
## [76] "STIN-JM" "STIN-TM" "STPO1" "STPO2" "STPO3"
## [81] "STPO-LS" "STPO-B" "STPO-F" "STPO-IHR" "STPO-P"
## [86] "STPO-PU" "STPO-R" "STPO-RH" "STPO-S" "STPO-YH"
## [91] "BH" "BB" "BR" "CC" "CP1"
## [96] "CP2" "CP3" "CL" "DP" "DPR"
## [101] "FR" "GM" "HH" "HM" "IH"
## [106] "JB" "KC1" "KC2" "KC3" "LV1"
## [111] "LV2" "LV3" "LVTR1" "LVTR2" "LVTR3"
## [116] "MSH" "PL" "RHC" "RB" "RG"
## [121] "SQ1" "SQ2" "SQ3" "SH" "SO"
## [126] "SC" "TM1" "TM2" "TFC" "WR"
## [131] "WV" "WL1" "WL2" "WL3" "WL4"
## [136] "YO1" "YO10" "YO11" "YO12" "YO13"
## [141] "YO2" "YO3" "YO4" "YO5" "YO6"
## [146] "YO7" "YO8" "YO9"#combine pop info with climate data
names(pop_elev)## [1] "parent.pop" "phylogroup" "elevation.group" "UCD.seed.year"
## [5] "Lat" "Long" "elev_m"names(climate_data_yo)## [1] "pop" "year" "month" "aet" "cwd" "pck" "pet" "ppt" "rch"
## [10] "run" "str" "tmn" "tmx"pop_elev_climate <- left_join(pop_elev, climate_data_yo, by=c("parent.pop"="pop")) %>% select(parent.pop, elevation.group, elev_m, year:tmx)
unique(pop_elev_climate$parent.pop)## [1] "BH" "CC" "CP2" "CP3" "DPR" "FR" "IH" "LV1" "LV3"
## [10] "LVTR1" "SC" "SQ1" "SQ2" "SQ3" "TM2" "WL1" "WL2" "WR"
## [19] "WV" "YO11" "YO4" "YO7" "YO8"#unique(pop_elev$parent.pop)
head(pop_elev_climate, 30)## # A tibble: 30 × 15
## parent.pop elevation.group elev_m year month aet cwd pck pet
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 1895 10 8.90 80.8 0 89.7
## 2 BH Low 511. 1895 11 4.89 40.8 0 45.7
## 3 BH Low 511. 1895 12 3.23 27.9 0 31.1
## 4 BH Low 511. 1896 1 8.75 27.0 0 35.7
## 5 BH Low 511. 1896 2 6.57 42.8 0 49.4
## 6 BH Low 511. 1896 3 24.9 57.9 0 82.8
## 7 BH Low 511. 1896 4 72.5 36.2 0 109.
## 8 BH Low 511. 1896 5 141. 11.6 0 153.
## 9 BH Low 511. 1896 6 90.7 96.2 0 187.
## 10 BH Low 511. 1896 7 133. 70.5 0 203.
## # ℹ 20 more rows
## # ℹ 6 more variables: ppt <dbl>, rch <dbl>, run <dbl>, str <dbl>, tmn <dbl>,
## # tmx <dbl>names(pop_elev_climate)## [1] "parent.pop" "elevation.group" "elev_m" "year"
## [5] "month" "aet" "cwd" "pck"
## [9] "pet" "ppt" "rch" "run"
## [13] "str" "tmn" "tmx"BioClim Variables (https://www.worldclim.org/data/bioclim.html#google_vignette)
- annual mean temperature (BIO1)
- mean diurnal range (BIO2)
- temperature seasonality (BIO4) (standard deviation *100)
- temperature annual range (BIO7) (Max Temperature of Warmest Month - Min Temperature of Coldest Month)
- mean temp of wettest quarter (BIO8)
- mean temp of driest quarter (BIO9)
- annual precipitation (BIO12) - sum of ppt for the entire year (not the avg)
- precipitation seasonality (BIO15) (Coefficient of Variation)
- precip of warmest quarter (BIO18)
- precip of coldest quarter (BIO19)
Use library QBMS, calc_biovars function: https://search.r-project.org/CRAN/refmans/QBMS/html/calc_biovars.html Data.frame has 4 mandatory columns (year, ppt, tmin, and tmax), and 12 rows (months) for each year sorted from Jan to Dec.
pop_elev_climate_bioclim_prep <- pop_elev_climate %>%
rename(tmin=tmn, tmax=tmx) %>% #renaming columns so they match what calc_biovars expects
filter(year != "1895", year !="2022") %>% #getting rid of the years where there are less than 12 months in the data
arrange(parent.pop, year, month) #making sure the data is sorted by pop, year, then month
head(pop_elev_climate_bioclim_prep, 60)## # A tibble: 60 × 15
## parent.pop elevation.group elev_m year month aet cwd pck pet
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 1896 1 8.75 27.0 0 35.7
## 2 BH Low 511. 1896 2 6.57 42.8 0 49.4
## 3 BH Low 511. 1896 3 24.9 57.9 0 82.8
## 4 BH Low 511. 1896 4 72.5 36.2 0 109.
## 5 BH Low 511. 1896 5 141. 11.6 0 153.
## 6 BH Low 511. 1896 6 90.7 96.2 0 187.
## 7 BH Low 511. 1896 7 133. 70.5 0 203.
## 8 BH Low 511. 1896 8 24.5 149. 0 174.
## 9 BH Low 511. 1896 9 0 127. 0 127.
## 10 BH Low 511. 1896 10 0.450 88.6 0 89
## # ℹ 50 more rows
## # ℹ 6 more variables: ppt <dbl>, rch <dbl>, run <dbl>, str <dbl>, tmin <dbl>,
## # tmax <dbl>pop_elev_climate_bioclim <- tibble(bio1="BLANK", bio2="BLANK", bio4="BLANK", bio7="BLANK", bio8="BLANK", bio9="BLANK", bio12="BLANK", bio15="BLANK", bio18="BLANK", bio19="BLANK", year="2024") #blank tibble to bind calculations to
pop_elev_climate_bioclim## # A tibble: 1 × 11
## bio1 bio2 bio4 bio7 bio8 bio9 bio12 bio15 bio18 bio19 year
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 BLANK BLANK BLANK BLANK BLANK BLANK BLANK BLANK BLANK BLANK 2024popids <- unique(pop_elev_climate_bioclim_prep$parent.pop) #list of pop ids for for loop
popids## [1] "BH" "CC" "CP2" "CP3" "DPR" "FR" "IH" "LV1" "LV3"
## [10] "LVTR1" "SC" "SQ1" "SQ2" "SQ3" "TM2" "WL1" "WL2" "WR"
## [19] "WV" "YO11" "YO4" "YO7" "YO8"for(i in popids) {
A <- pop_elev_climate_bioclim_prep %>% filter(parent.pop==i) %>% calc_biovars() %>% mutate(parent.pop=i)
#print(A)
pop_elev_climate_bioclim <- bind_rows(pop_elev_climate_bioclim, A)
}
unique(pop_elev_climate_bioclim$parent.pop) #has all the populations in there!## [1] NA "BH" "CC" "CP2" "CP3" "DPR" "FR" "IH" "LV1"
## [10] "LV3" "LVTR1" "SC" "SQ1" "SQ2" "SQ3" "TM2" "WL1" "WL2"
## [19] "WR" "WV" "YO11" "YO4" "YO7" "YO8"pop_elev_climate_bioclim_final <- pop_elev_climate_bioclim %>%
mutate(across(c(bio1, bio2, bio4, bio7, bio8, bio9, bio12, bio15, bio18, bio19), as.numeric)) %>%
select(parent.pop, year, ann_tmean=bio1, mean_diurnal_range=bio2, temp_seasonality=bio4, temp_ann_range=bio7, tmean_wettest_quarter=bio8, tmean_driest_quarter=bio9, ann_ppt=bio12, ppt_seasonality=bio15, ppt_warmest_quarter=bio18, ppt_coldest_quarter=bio19) %>%
filter(year!="2024")## Warning: There were 10 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `across(...)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 9 remaining warnings.head(pop_elev_climate_bioclim_final)## # A tibble: 6 × 12
## parent.pop year ann_tmean mean_diurnal_range temp_seasonality temp_ann_range
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 BH 1896 14.8 14.0 616. 30.8
## 2 BH 1897 14.4 13.8 707. 32.8
## 3 BH 1898 14.5 14.7 700. 35.7
## 4 BH 1899 14.9 15.2 649. 34.5
## 5 BH 1900 15.0 15.0 597. 34.5
## 6 BH 1901 15.3 15.4 648. 34.2
## # ℹ 6 more variables: tmean_wettest_quarter <dbl>, tmean_driest_quarter <dbl>,
## # ann_ppt <dbl>, ppt_seasonality <dbl>, ppt_warmest_quarter <dbl>,
## # ppt_coldest_quarter <dbl>summary(pop_elev_climate_bioclim_final)## parent.pop year ann_tmean mean_diurnal_range
## Length:2898 Length:2898 Min. : 0.5833 Min. :10.53
## Class :character Class :character 1st Qu.: 5.2518 1st Qu.:12.80
## Mode :character Mode :character Median : 8.3852 Median :13.67
## Mean : 9.1695 Mean :13.71
## 3rd Qu.:13.0540 3rd Qu.:14.45
## Max. :17.9667 Max. :18.36
## temp_seasonality temp_ann_range tmean_wettest_quarter tmean_driest_quarter
## Min. :487.5 Min. :25.76 Min. :-9.0350 Min. : 1.482
## 1st Qu.:627.7 1st Qu.:31.20 1st Qu.:-0.8413 1st Qu.:13.177
## Median :663.4 Median :32.67 Median : 2.2042 Median :16.637
## Mean :663.2 Mean :32.83 Mean : 2.7025 Mean :17.145
## 3rd Qu.:698.5 3rd Qu.:34.27 3rd Qu.: 6.5108 3rd Qu.:21.566
## Max. :835.8 Max. :42.90 Max. :15.0133 Max. :26.722
## ann_ppt ppt_seasonality ppt_warmest_quarter ppt_coldest_quarter
## Min. : 150.4 Min. : 51.39 Min. : 0.000 Min. : 56.12
## 1st Qu.: 842.9 1st Qu.: 99.74 1st Qu.: 5.598 1st Qu.: 393.22
## Median :1109.6 Median :112.15 Median : 17.665 Median : 573.98
## Mean :1196.5 Mean :114.35 Mean : 31.230 Mean : 619.32
## 3rd Qu.:1463.5 3rd Qu.:127.26 3rd Qu.: 42.498 3rd Qu.: 796.76
## Max. :3422.3 Max. :224.86 Max. :339.090 Max. :1964.74pop_elev_climate_bioclim_elev <- left_join(pop_elev_climate_bioclim_final, pop_elev)## Joining with `by = join_by(parent.pop)`head(pop_elev_climate_bioclim_elev)## # A tibble: 6 × 18
## parent.pop year ann_tmean mean_diurnal_range temp_seasonality temp_ann_range
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 BH 1896 14.8 14.0 616. 30.8
## 2 BH 1897 14.4 13.8 707. 32.8
## 3 BH 1898 14.5 14.7 700. 35.7
## 4 BH 1899 14.9 15.2 649. 34.5
## 5 BH 1900 15.0 15.0 597. 34.5
## 6 BH 1901 15.3 15.4 648. 34.2
## # ℹ 12 more variables: tmean_wettest_quarter <dbl>, tmean_driest_quarter <dbl>,
## # ann_ppt <dbl>, ppt_seasonality <dbl>, ppt_warmest_quarter <dbl>,
## # ppt_coldest_quarter <dbl>, phylogroup <dbl>, elevation.group <chr>,
## # UCD.seed.year <dbl>, Lat <dbl>, Long <dbl>, elev_m <dbl>#double checking that calc_biovars is working
BH_TEST_BIOCLIM <- pop_elev_climate_bioclim_prep %>% filter(parent.pop=="BH") %>% calc_biovars()
#bio1
pop_elev_climate_bioclim_prep %>% filter(parent.pop=="BH") %>% filter(year=="1896") %>% mutate(tempsum=tmin+tmax) %>% mutate(avgtemp=tempsum/2) %>% group_by(year) %>% summarise(bio1=mean(avgtemp)) #14.81875, same as calc_biovars## # A tibble: 1 × 2
## year bio1
## <chr> <dbl>
## 1 1896 14.8#bio12 741.539997644722 from calc_biovars
pop_elev_climate_bioclim_prep %>% filter(parent.pop=="BH") %>% filter(year=="1896") %>% summarise(bio12=sum(ppt)) #741.54## # A tibble: 1 × 1
## bio12
## <dbl>
## 1 742.pop_elev_climate_bioclim_prep %>% filter(parent.pop=="BH") %>% filter(year=="1896") %>% summarise(bio12=mean(ppt))## # A tibble: 1 × 1
## bio12
## <dbl>
## 1 61.8Calculation of recent (last 30 years) and historical climate (prior 30 years)
Took out 2022 b/c only 9 months for that year Flint variables
pop_elev_climate_recent <- pop_elev_climate %>% filter(year>1991 & year<2022) %>% select(parent.pop:month, cwd, pck, ppt, tmn, tmx)
head(pop_elev_climate_recent)## # A tibble: 6 × 10
## parent.pop elevation.group elev_m year month cwd pck ppt tmn tmx
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 1992 1 28.3 0 42.7 1.22 10.6
## 2 BH Low 511. 1992 2 40.1 0 175. 5.26 16.0
## 3 BH Low 511. 1992 3 52.0 0 76.3 6.21 17.4
## 4 BH Low 511. 1992 4 75.9 0 2.19 8.31 23.6
## 5 BH Low 511. 1992 5 78.6 0 0.0800 12.7 29.4
## 6 BH Low 511. 1992 6 99.1 0 1.77 13.6 30.2tail(pop_elev_climate_recent)## # A tibble: 6 × 10
## parent.pop elevation.group elev_m year month cwd pck ppt tmn tmx
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 YO8 High 2591. 2021 7 114. 0 30.0 10.8 24.1
## 2 YO8 High 2591. 2021 8 127. 0 3.33 9.11 22.7
## 3 YO8 High 2591. 2021 9 107. 0 3.39 5.92 20.4
## 4 YO8 High 2591. 2021 10 68.4 0 202. -0.660 10.6
## 5 YO8 High 2591. 2021 11 41.0 0 30.0 -0.820 10.8
## 6 YO8 High 2591. 2021 12 23.3 400. 408. -5.79 2.32summary(pop_elev_climate_recent)## parent.pop elevation.group elev_m year
## Length:8280 Length:8280 Min. : 313.0 Length:8280
## Class :character Class :character 1st Qu.: 748.9 Class :character
## Mode :character Mode :character Median :1934.5 Mode :character
## Mean :1649.7
## 3rd Qu.:2373.2
## Max. :2872.3
## month cwd pck ppt
## Min. : 1.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 3.75 1st Qu.: 25.38 1st Qu.: 0.00 1st Qu.: 7.48
## Median : 6.50 Median : 46.28 Median : 0.00 Median : 48.34
## Mean : 6.50 Mean : 55.83 Mean : 131.07 Mean : 99.06
## 3rd Qu.: 9.25 3rd Qu.: 82.86 3rd Qu.: 84.82 3rd Qu.:143.49
## Max. :12.00 Max. :194.73 Max. :2183.62 Max. :981.42
## tmn tmx
## Min. :-13.180 Min. :-3.220
## 1st Qu.: -1.863 1st Qu.: 9.398
## Median : 3.490 Median :15.680
## Mean : 3.531 Mean :16.506
## 3rd Qu.: 8.650 3rd Qu.:23.020
## Max. : 21.350 Max. :37.380xtabs(~parent.pop+month, data=pop_elev_climate_recent)## month
## parent.pop 1 2 3 4 5 6 7 8 9 10 11 12
## BH 30 30 30 30 30 30 30 30 30 30 30 30
## CC 30 30 30 30 30 30 30 30 30 30 30 30
## CP2 30 30 30 30 30 30 30 30 30 30 30 30
## CP3 30 30 30 30 30 30 30 30 30 30 30 30
## DPR 30 30 30 30 30 30 30 30 30 30 30 30
## FR 30 30 30 30 30 30 30 30 30 30 30 30
## IH 30 30 30 30 30 30 30 30 30 30 30 30
## LV1 30 30 30 30 30 30 30 30 30 30 30 30
## LV3 30 30 30 30 30 30 30 30 30 30 30 30
## LVTR1 30 30 30 30 30 30 30 30 30 30 30 30
## SC 30 30 30 30 30 30 30 30 30 30 30 30
## SQ1 30 30 30 30 30 30 30 30 30 30 30 30
## SQ2 30 30 30 30 30 30 30 30 30 30 30 30
## SQ3 30 30 30 30 30 30 30 30 30 30 30 30
## TM2 30 30 30 30 30 30 30 30 30 30 30 30
## WL1 30 30 30 30 30 30 30 30 30 30 30 30
## WL2 30 30 30 30 30 30 30 30 30 30 30 30
## WR 30 30 30 30 30 30 30 30 30 30 30 30
## WV 30 30 30 30 30 30 30 30 30 30 30 30
## YO11 30 30 30 30 30 30 30 30 30 30 30 30
## YO4 30 30 30 30 30 30 30 30 30 30 30 30
## YO7 30 30 30 30 30 30 30 30 30 30 30 30
## YO8 30 30 30 30 30 30 30 30 30 30 30 30pop_elev_climate_historical <- pop_elev_climate %>% filter(year<=1991 & year>1961) %>% select(parent.pop:month, cwd, pck, ppt, tmn, tmx)
head(pop_elev_climate_historical, 13)## # A tibble: 13 × 10
## parent.pop elevation.group elev_m year month cwd pck ppt tmn tmx
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 1962 1 28.5 0 59.8 -1.44 11.9
## 2 BH Low 511. 1962 2 37.4 0 289. 2.55 12.5
## 3 BH Low 511. 1962 3 45.3 0 80.6 2.15 15.0
## 4 BH Low 511. 1962 4 74.5 0 8.19 6.16 23.9
## 5 BH Low 511. 1962 5 70.6 0 3.08 6.72 23.8
## 6 BH Low 511. 1962 6 97.2 0 0.820 11.6 31.5
## 7 BH Low 511. 1962 7 127. 0 1.31 14.5 35.3
## 8 BH Low 511. 1962 8 141. 0 0 13.9 34.4
## 9 BH Low 511. 1962 9 125. 0 1.44 11.9 32.1
## 10 BH Low 511. 1962 10 86.2 0 48.8 8.47 24.1
## 11 BH Low 511. 1962 11 44.6 0 9.85 4.13 19.4
## 12 BH Low 511. 1962 12 31.2 0 57.8 2.20 15.4
## 13 BH Low 511. 1963 1 28.1 0 109. -1.85 12.3xtabs(~parent.pop+month, data=pop_elev_climate_historical)## month
## parent.pop 1 2 3 4 5 6 7 8 9 10 11 12
## BH 30 30 30 30 30 30 30 30 30 30 30 30
## CC 30 30 30 30 30 30 30 30 30 30 30 30
## CP2 30 30 30 30 30 30 30 30 30 30 30 30
## CP3 30 30 30 30 30 30 30 30 30 30 30 30
## DPR 30 30 30 30 30 30 30 30 30 30 30 30
## FR 30 30 30 30 30 30 30 30 30 30 30 30
## IH 30 30 30 30 30 30 30 30 30 30 30 30
## LV1 30 30 30 30 30 30 30 30 30 30 30 30
## LV3 30 30 30 30 30 30 30 30 30 30 30 30
## LVTR1 30 30 30 30 30 30 30 30 30 30 30 30
## SC 30 30 30 30 30 30 30 30 30 30 30 30
## SQ1 30 30 30 30 30 30 30 30 30 30 30 30
## SQ2 30 30 30 30 30 30 30 30 30 30 30 30
## SQ3 30 30 30 30 30 30 30 30 30 30 30 30
## TM2 30 30 30 30 30 30 30 30 30 30 30 30
## WL1 30 30 30 30 30 30 30 30 30 30 30 30
## WL2 30 30 30 30 30 30 30 30 30 30 30 30
## WR 30 30 30 30 30 30 30 30 30 30 30 30
## WV 30 30 30 30 30 30 30 30 30 30 30 30
## YO11 30 30 30 30 30 30 30 30 30 30 30 30
## YO4 30 30 30 30 30 30 30 30 30 30 30 30
## YO7 30 30 30 30 30 30 30 30 30 30 30 30
## YO8 30 30 30 30 30 30 30 30 30 30 30 30Bioclim variables
bioclim_recent <- pop_elev_climate_bioclim_elev %>% filter(year>1991 & year<2022) %>% #note this does not include 2022 b/c there were only 9 months of data available
select(parent.pop, elevation.group, elev_m, year, ann_tmean:ppt_coldest_quarter)
head(bioclim_recent)## # A tibble: 6 × 14
## parent.pop elevation.group elev_m year ann_tmean mean_diurnal_range
## <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 BH Low 511. 1992 16.3 14.5
## 2 BH Low 511. 1993 15.5 14.8
## 3 BH Low 511. 1994 15.6 15.0
## 4 BH Low 511. 1995 16.1 14.3
## 5 BH Low 511. 1996 16.4 14.7
## 6 BH Low 511. 1997 16.4 14.8
## # ℹ 8 more variables: temp_seasonality <dbl>, temp_ann_range <dbl>,
## # tmean_wettest_quarter <dbl>, tmean_driest_quarter <dbl>, ann_ppt <dbl>,
## # ppt_seasonality <dbl>, ppt_warmest_quarter <dbl>, ppt_coldest_quarter <dbl>tail(bioclim_recent)## # A tibble: 6 × 14
## parent.pop elevation.group elev_m year ann_tmean mean_diurnal_range
## <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 YO8 High 2591. 2016 5.99 13.1
## 2 YO8 High 2591. 2017 5.93 12.8
## 3 YO8 High 2591. 2018 6.14 13.3
## 4 YO8 High 2591. 2019 4.76 12.5
## 5 YO8 High 2591. 2020 6.52 13.7
## 6 YO8 High 2591. 2021 6.26 12.5
## # ℹ 8 more variables: temp_seasonality <dbl>, temp_ann_range <dbl>,
## # tmean_wettest_quarter <dbl>, tmean_driest_quarter <dbl>, ann_ppt <dbl>,
## # ppt_seasonality <dbl>, ppt_warmest_quarter <dbl>, ppt_coldest_quarter <dbl>summary(bioclim_recent)## parent.pop elevation.group elev_m year
## Length:690 Length:690 Min. : 313.0 Length:690
## Class :character Class :character 1st Qu.: 748.9 Class :character
## Mode :character Mode :character Median :1934.5 Mode :character
## Mean :1649.7
## 3rd Qu.:2373.2
## Max. :2872.3
## ann_tmean mean_diurnal_range temp_seasonality temp_ann_range
## Min. : 2.112 Min. :10.76 Min. :542.2 Min. :27.43
## 1st Qu.: 6.178 1st Qu.:12.21 1st Qu.:644.1 1st Qu.:30.76
## Median : 9.397 Median :12.82 Median :677.1 Median :32.03
## Mean :10.019 Mean :12.97 Mean :678.1 Mean :32.00
## 3rd Qu.:13.808 3rd Qu.:13.64 3rd Qu.:708.9 3rd Qu.:33.14
## Max. :17.967 Max. :15.84 Max. :802.2 Max. :37.72
## tmean_wettest_quarter tmean_driest_quarter ann_ppt ppt_seasonality
## Min. :-5.6500 Min. : 6.582 Min. : 150.4 Min. : 51.39
## 1st Qu.:-0.2267 1st Qu.:14.363 1st Qu.: 855.4 1st Qu.:104.96
## Median : 3.0092 Median :17.719 Median :1096.3 Median :115.55
## Mean : 3.4767 Mean :18.228 Mean :1188.8 Mean :117.14
## 3rd Qu.: 7.1037 3rd Qu.:22.481 3rd Qu.:1449.2 3rd Qu.:128.99
## Max. :15.0133 Max. :26.672 Max. :2903.9 Max. :224.86
## ppt_warmest_quarter ppt_coldest_quarter
## Min. : 0.000 Min. : 56.12
## 1st Qu.: 4.362 1st Qu.: 440.76
## Median : 15.030 Median : 615.12
## Mean : 23.654 Mean : 646.30
## 3rd Qu.: 33.700 3rd Qu.: 807.90
## Max. :131.590 Max. :1747.63bioclim_historical <- pop_elev_climate_bioclim_elev %>% filter(year<=1991 & year>1961) %>%
select(parent.pop, elevation.group, elev_m, year, ann_tmean:ppt_coldest_quarter)
head(bioclim_historical)## # A tibble: 6 × 14
## parent.pop elevation.group elev_m year ann_tmean mean_diurnal_range
## <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 BH Low 511. 1962 15.1 16.4
## 2 BH Low 511. 1963 14.6 14.9
## 3 BH Low 511. 1964 14.8 15.8
## 4 BH Low 511. 1965 14.6 15.3
## 5 BH Low 511. 1966 15.7 16.1
## 6 BH Low 511. 1967 15.2 15.6
## # ℹ 8 more variables: temp_seasonality <dbl>, temp_ann_range <dbl>,
## # tmean_wettest_quarter <dbl>, tmean_driest_quarter <dbl>, ann_ppt <dbl>,
## # ppt_seasonality <dbl>, ppt_warmest_quarter <dbl>, ppt_coldest_quarter <dbl>tail(bioclim_historical)## # A tibble: 6 × 14
## parent.pop elevation.group elev_m year ann_tmean mean_diurnal_range
## <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 YO8 High 2591. 1986 4.96 13.4
## 2 YO8 High 2591. 1987 4.67 14.2
## 3 YO8 High 2591. 1988 5.24 14.6
## 4 YO8 High 2591. 1989 4.74 13.7
## 5 YO8 High 2591. 1990 4.58 13.9
## 6 YO8 High 2591. 1991 4.97 13.9
## # ℹ 8 more variables: temp_seasonality <dbl>, temp_ann_range <dbl>,
## # tmean_wettest_quarter <dbl>, tmean_driest_quarter <dbl>, ann_ppt <dbl>,
## # ppt_seasonality <dbl>, ppt_warmest_quarter <dbl>, ppt_coldest_quarter <dbl>summary(bioclim_historical)## parent.pop elevation.group elev_m year
## Length:690 Length:690 Min. : 313.0 Length:690
## Class :character Class :character 1st Qu.: 748.9 Class :character
## Mode :character Mode :character Median :1934.5 Mode :character
## Mean :1649.7
## 3rd Qu.:2373.2
## Max. :2872.3
## ann_tmean mean_diurnal_range temp_seasonality temp_ann_range
## Min. : 1.302 Min. :10.53 Min. :487.5 Min. :25.76
## 1st Qu.: 4.995 1st Qu.:12.84 1st Qu.:635.8 1st Qu.:31.27
## Median : 8.011 Median :13.67 Median :668.3 Median :32.66
## Mean : 8.878 Mean :13.69 Mean :662.5 Mean :32.66
## 3rd Qu.:12.831 3rd Qu.:14.39 3rd Qu.:696.9 3rd Qu.:34.15
## Max. :16.660 Max. :17.01 Max. :835.8 Max. :39.20
## tmean_wettest_quarter tmean_driest_quarter ann_ppt ppt_seasonality
## Min. :-6.640 Min. : 1.482 Min. : 271.0 Min. : 60.47
## 1st Qu.:-1.052 1st Qu.:12.398 1st Qu.: 841.7 1st Qu.: 97.94
## Median : 1.838 Median :15.991 Median :1110.9 Median :110.46
## Mean : 2.526 Mean :16.572 Mean :1208.4 Mean :113.99
## 3rd Qu.: 6.408 3rd Qu.:21.167 3rd Qu.:1488.5 3rd Qu.:127.10
## Max. :12.760 Max. :26.722 Max. :3422.3 Max. :203.24
## ppt_warmest_quarter ppt_coldest_quarter
## Min. : 0.00 Min. : 77.49
## 1st Qu.: 11.46 1st Qu.: 379.35
## Median : 28.16 Median : 559.21
## Mean : 43.39 Mean : 594.48
## 3rd Qu.: 57.34 3rd Qu.: 786.36
## Max. :295.97 Max. :1559.57Bioclim trends
bioclim_recent %>% ggplot(aes(x=parent.pop, y=ann_tmean)) + geom_boxplot()
bioclim_historical %>% ggplot(aes(x=parent.pop, y=ann_tmean)) + geom_boxplot()
bioclim_recent %>% ggplot(aes(x=parent.pop, y=ann_ppt)) + geom_boxplot() #seemingly more variation in ppt across years than temperature 
bioclim_historical %>% ggplot(aes(x=parent.pop, y=ann_ppt)) + geom_boxplot()
bioclim_recent %>% ggplot(aes(x=year, y=temp_seasonality, group=parent.pop, color=parent.pop)) + geom_line()
bioclim_historical %>% ggplot(aes(x=year, y=temp_seasonality, group=parent.pop, color=parent.pop)) + geom_line()
bioclim_recent %>% ggplot(aes(x=year, y=ppt_seasonality, group=parent.pop, color=parent.pop)) + geom_line()
bioclim_historical %>% ggplot(aes(x=year, y=ppt_seasonality, group=parent.pop, color=parent.pop)) + geom_line()
Snow Cover - Average across years (all months included)
names(pop_elev_climate_recent)## [1] "parent.pop" "elevation.group" "elev_m" "year"
## [5] "month" "cwd" "pck" "ppt"
## [9] "tmn" "tmx"pop_elev_climate_recent %>% filter(year==2006) %>% ggplot(aes(x=month, y=pck, group=parent.pop, color=parent.pop)) + geom_point() + geom_line()
pop_elev_climate_recent %>% filter(year==2006) %>% ggplot(aes(x=month, y=pck, group=parent.pop, color=elevation.group)) + geom_point() + geom_line()
#average snowpack across entire year for 30-year period
recent_climate_avgs <- pop_elev_climate_recent %>% group_by(parent.pop,elevation.group ,elev_m) %>% summarise_at(c("cwd", "pck", "ppt", "tmn", "tmx"), c(mean, sem), na.rm = TRUE)
names(recent_climate_avgs) <- gsub("fn2", "sem", colnames(recent_climate_avgs))
names(recent_climate_avgs) <-gsub("fn1", "mean", colnames(recent_climate_avgs))
recent_climate_avgs #30 year averages## # A tibble: 23 × 13
## # Groups: parent.pop, elevation.group [23]
## parent.pop elevation.group elev_m cwd_mean pck_mean ppt_mean tmn_mean
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 75.5 0 48.6 8.81
## 2 CC Low 313 59.6 0 85.7 9.97
## 3 CP2 High 2244. 62.7 203. 106. 1.12
## 4 CP3 High 2266. 46.2 221. 102. 0.475
## 5 DPR Mid 1019. 27.5 8.95 122. 7.80
## 6 FR Mid 787 55.8 19.0 83.1 5.33
## 7 IH Low 454. 49.1 0.194 89.9 8.63
## 8 LV1 High 2593. 49.4 438. 148. -1.44
## 9 LV3 High 2354. 57.2 423. 147. -1.42
## 10 LVTR1 High 2741. 51.6 451. 153. -1.63
## # ℹ 13 more rows
## # ℹ 6 more variables: tmx_mean <dbl>, cwd_sem <dbl>, pck_sem <dbl>,
## # ppt_sem <dbl>, tmn_sem <dbl>, tmx_sem <dbl>recent_climate_avgs$elevation.group <- factor(recent_climate_avgs$elevation.group, levels=elev_order)
recent_snwpck <- recent_climate_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, pck_mean), y=pck_mean, fill=elevation.group)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=pck_mean-pck_sem,ymax=pck_mean+pck_sem),width=.2, position =
position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_manual(values=elev_three_palette) +
#scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(x="Population", y="Avg SnwPck (mm)" ,title = "Recent Climate") +
theme_classic() +
theme(text=element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1))
recent_snwpck
#ggsave("../output/Climate/All_Year_Avg_PCK_RecentClim.png", width = 12, height = 6, units = "in")
historical_climate_avgs <- pop_elev_climate_historical %>% group_by(parent.pop, elevation.group ,elev_m) %>% summarise_at(c("cwd", "pck", "ppt", "tmn", "tmx"), c(mean, sem), na.rm = TRUE)
names(historical_climate_avgs) <- gsub("fn2", "sem", colnames(historical_climate_avgs))
names(historical_climate_avgs) <-gsub("fn1", "mean", colnames(historical_climate_avgs))
historical_climate_avgs #30 year averages## # A tibble: 23 × 13
## # Groups: parent.pop, elevation.group [23]
## parent.pop elevation.group elev_m cwd_mean pck_mean ppt_mean tmn_mean
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 74.4 0.0195 48.4 7.63
## 2 CC Low 313 59.9 0.0793 82.1 8.85
## 3 CP2 High 2244. 60.3 263. 112. -0.396
## 4 CP3 High 2266. 43.7 281. 108. -0.938
## 5 DPR Mid 1019. 26.3 19.0 122. 6.15
## 6 FR Mid 787 54.7 21.7 82.8 4.08
## 7 IH Low 454. 50.2 1.45 88.9 7.61
## 8 LV1 High 2593. 46.7 510. 151. -3.51
## 9 LV3 High 2354. 54.6 494. 149. -3.52
## 10 LVTR1 High 2741. 49.3 530. 156. -3.66
## # ℹ 13 more rows
## # ℹ 6 more variables: tmx_mean <dbl>, cwd_sem <dbl>, pck_sem <dbl>,
## # ppt_sem <dbl>, tmn_sem <dbl>, tmx_sem <dbl>historical_climate_avgs$elevation.group <- factor(historical_climate_avgs$elevation.group, levels=elev_order)
hist_snwpck <- historical_climate_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, pck_mean), y=pck_mean, fill=elevation.group)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=pck_mean-pck_sem,ymax=pck_mean+pck_sem),width=.2, position =
position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_manual(values=elev_three_palette) +
#scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(x="Population", y="Avg SnwPck (mm)" ,title = "Historical Climate") +
theme_classic() +
theme(text=element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1))
hist_snwpck
#ggsave("../output/Climate/All_Year_Avg_PCK_HistoricalClim.png", width = 8, height = 4, units = "in")
legend <- get_legend(hist_snwpck)
hist_snwpck <- hist_snwpck + theme(legend.position="none")
recent_snwpck <- recent_snwpck + theme(legend.position="none")
grid.arrange(hist_snwpck, recent_snwpck, legend, ncol=3, widths=c(3.12, 3.12, 1.09))
Consistency of winter snow cover
#monthly_pck <- pop_elev_climate_recent %>% filter(parent.pop==c("DPR","WR","WL1", "SQ1", "SQ2", "WL2", "YO4")) %>% group_by(parent.pop, elev_m, month) %>%
# summarise(pck_mean=mean(pck), pck_sem=sem(pck))
#monthly_pck$parent.pop <- factor(monthly_pck$parent.pop, levels=c("DPR","WR","WL1", "SQ1", "SQ2", "WL2", "YO4"))
monthly_pck <- pop_elev_climate_recent %>%
filter(parent.pop==c("WL1", "SQ1", "SQ2", "WL2", "YO4")) %>%
group_by(parent.pop, elev_m, month) %>%
summarise(pck_mean=mean(pck), pck_sem=sem(pck))## `summarise()` has grouped output by 'parent.pop', 'elev_m'. You can override
## using the `.groups` argument.monthly_pck$parent.pop <- factor(monthly_pck$parent.pop, levels=c("WL1", "SQ1", "SQ2", "WL2", "YO4"))
monthly_pck## # A tibble: 60 × 5
## # Groups: parent.pop, elev_m [5]
## parent.pop elev_m month pck_mean pck_sem
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 SQ1 1921. 1 128. 51.0
## 2 SQ1 1921. 2 211. 101.
## 3 SQ1 1921. 3 96.3 66.8
## 4 SQ1 1921. 4 62.5 35.3
## 5 SQ1 1921. 5 23.2 23.2
## 6 SQ1 1921. 6 0 0
## 7 SQ1 1921. 7 0 0
## 8 SQ1 1921. 8 0 0
## 9 SQ1 1921. 9 0 0
## 10 SQ1 1921. 10 0 0
## # ℹ 50 more rowsmonthly_pck %>% ggplot(aes(x=month, y=pck_mean, group=parent.pop, fill=parent.pop)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=pck_mean-pck_sem,ymax=pck_mean+pck_sem),width=.2, position=position_dodge(0.75)) +
labs(title="Recent Climate", y="Avg SnwPck (mm)") +
scale_y_continuous(expand = c(0, 0)) +
scale_x_continuous(breaks=c(1, 2, 3, 4, 5,6,7,8,9,10, 11, 12)) +
theme_classic() +
theme(text=element_text(size=30))
#ggsave("../output/Climate/Monthly_Avg_PCK_RecnetClim.png", width = 14, height = 6, units = "in")
#check even higher elevation
monthly_pck_high_elev <- pop_elev_climate_recent %>%
filter(elev_m>2200) %>%
group_by(parent.pop, elev_m, month) %>%
summarise(pck_mean=mean(pck), pck_sem=sem(pck))## `summarise()` has grouped output by 'parent.pop', 'elev_m'. You can override
## using the `.groups` argument.monthly_pck_high_elev$parent.pop <- factor(monthly_pck_high_elev$parent.pop, levels=c("CP2", "CP3", "LV3", "SQ3", "YO7","YO8", "LV1", "LVTR1", "YO11"))
monthly_pck_high_elev %>% ggplot(aes(x=month, y=pck_mean, group=parent.pop, fill=parent.pop)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=pck_mean-pck_sem,ymax=pck_mean+pck_sem),width=.2, position=position_dodge(0.75)) +
labs(title="Recent Climate", y="Avg SnwPck (mm)") +
scale_y_continuous(expand = c(0, 0)) +
scale_x_continuous(breaks=c(1, 2, 3, 4, 5,6,7,8,9,10, 11, 12)) +
theme_classic() +
theme(text=element_text(size=30))
#ggsave("../output/Climate/Monthly_Avg_PCK_HIGHELEV_RecnetClim.png", width = 14, height = 6, units = "in")CWD Across the Year
names(pop_elev_climate_recent)## [1] "parent.pop" "elevation.group" "elev_m" "year"
## [5] "month" "cwd" "pck" "ppt"
## [9] "tmn" "tmx"monthly_cwd <- pop_elev_climate_recent %>%
group_by(parent.pop, elev_m, month) %>%
summarise(cwd_mean=mean(cwd), pck_sem=sem(cwd))## `summarise()` has grouped output by 'parent.pop', 'elev_m'. You can override
## using the `.groups` argument.monthly_cwd## # A tibble: 276 × 5
## # Groups: parent.pop, elev_m [23]
## parent.pop elev_m month cwd_mean pck_sem
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 BH 511. 1 29.3 0.430
## 2 BH 511. 2 40.9 0.492
## 3 BH 511. 3 53.8 1.52
## 4 BH 511. 4 59.0 2.92
## 5 BH 511. 5 51.5 5.37
## 6 BH 511. 6 87.1 5.45
## 7 BH 511. 7 136. 4.35
## 8 BH 511. 8 154. 3.31
## 9 BH 511. 9 130. 1.03
## 10 BH 511. 10 88.9 0.537
## # ℹ 266 more rowsmonthly_cwd %>% #low elev
filter(elev_m<520) %>%
ggplot(aes(x=month, y=cwd_mean)) +
geom_line() +
scale_x_continuous(breaks=c(1, 2, 3, 4, 5,6,7,8,9,10, 11, 12)) +
theme_classic() +
facet_wrap(~parent.pop)
monthly_cwd %>% #mid elev
filter(elev_m>520, elev_m<1940) %>%
ggplot(aes(x=month, y=cwd_mean)) +
geom_line() +
scale_x_continuous(breaks=c(1, 2, 3, 4, 5,6,7,8,9,10, 11, 12)) +
theme_classic() +
facet_wrap(~parent.pop)
monthly_cwd %>% #high elev
filter(elev_m>1940) %>%
ggplot(aes(x=month, y=cwd_mean)) +
geom_line() +
scale_x_continuous(breaks=c(1, 2, 3, 4, 5,6,7,8,9,10, 11, 12)) +
theme_classic() +
facet_wrap(~parent.pop)
Growth Season by snow/rain
First month with no snow pack or w/ significant rain as start of growth season First month with snow pack or w/ significant CWD as end of growth season Mean height at WL2 in Oct was 7 cm –> 70 mm
Averages and Preliminary Calcs
Recent climate
pop_elev_climate_recent %>% filter(ppt == 0) #months 7-10 usually have ppt = 0 for low elev ## # A tibble: 553 × 10
## parent.pop elevation.group elev_m year month cwd pck ppt tmn tmx
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 1992 8 160. 0 0 17.1 35.4
## 2 BH Low 511. 1992 9 132. 0 0 13.6 31.7
## 3 BH Low 511. 1993 7 128. 0 0 15.0 32.9
## 4 BH Low 511. 1993 8 145. 0 0 14.8 33.2
## 5 BH Low 511. 1993 9 128. 0 0 12.7 31.9
## 6 BH Low 511. 1994 7 174. 0 0 17.3 35.8
## 7 BH Low 511. 1994 8 180. 0 0 16.3 35.5
## 8 BH Low 511. 1995 10 92.1 0 0 9.55 27.6
## 9 BH Low 511. 1996 9 132. 0 0 12.2 31.0
## 10 BH Low 511. 1997 8 155. 0 0 16.3 33.7
## # ℹ 543 more rowspop_elev_climate_recent %>% filter(pck >0, ppt == 0) #in sequoia pops, 2022, ppt=0, but there was still snowpack in January ## # A tibble: 0 × 10
## # ℹ 10 variables: parent.pop <chr>, elevation.group <chr>, elev_m <dbl>,
## # year <chr>, month <dbl>, cwd <dbl>, pck <dbl>, ppt <dbl>, tmn <dbl>,
## # tmx <dbl>pop_elev_climate_recent %>% filter(ppt > 0, tmx<1.5) %>% arrange(pck) #if the temp is less than 1.5 (Flint's criteria for snowfall) and there is precipitation then pck > 0## # A tibble: 168 × 10
## parent.pop elevation.group elev_m year month cwd pck ppt tmn tmx
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 YO11 High 2872. 2001 1 19.0 98.1 91.4 -11.6 1.08
## 2 YO11 High 2872. 1997 12 16.9 106. 69.8 -11.5 0.150
## 3 YO11 High 2872. 2007 12 17.8 107. 114. -10.3 0.870
## 4 YO11 High 2872. 2009 12 18.1 140. 146. -10.3 1.47
## 5 YO11 High 2872. 2019 12 18.7 146. 153. -7.92 0.590
## 6 YO11 High 2872. 1994 11 16.7 191. 198. -11.1 0.330
## 7 LV3 High 2354. 2007 12 15.8 194. 200. -8.33 0.300
## 8 LV1 High 2593. 2007 12 12.2 198. 204. -8.54 0.340
## 9 LVTR1 High 2741. 2007 12 13.5 205. 211. -8.72 0.25
## 10 LV3 High 2354. 2009 12 15.6 210. 186. -9.76 1.30
## # ℹ 158 more rows#first calculate 30 year monthly averages for pck and ppt
pop_elev_climate_recent_avgs <- pop_elev_climate_recent %>% group_by(parent.pop, elevation.group, elev_m, month) %>% summarise_at(c("cwd", "pck", "ppt", "tmn", "tmx"), c(mean, sem), na.rm = TRUE)
names(pop_elev_climate_recent_avgs) <- gsub("fn2", "sem", colnames(pop_elev_climate_recent_avgs))
names(pop_elev_climate_recent_avgs) <-gsub("fn1", "mean", colnames(pop_elev_climate_recent_avgs))
pop_elev_climate_recent_avgs #30 year averages per month for each pop## # A tibble: 276 × 14
## # Groups: parent.pop, elevation.group, elev_m [23]
## parent.pop elevation.group elev_m month cwd_mean pck_mean ppt_mean tmn_mean
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 1 29.3 0 125. 2.68
## 2 BH Low 511. 2 40.9 0 100. 3.43
## 3 BH Low 511. 3 53.8 0 85.4 4.90
## 4 BH Low 511. 4 59.0 0 47.2 6.37
## 5 BH Low 511. 5 51.5 0 23.1 9.76
## 6 BH Low 511. 6 87.1 0 7.43 13.4
## 7 BH Low 511. 7 136. 0 1.40 17.2
## 8 BH Low 511. 8 154. 0 0.507 16.8
## 9 BH Low 511. 9 130. 0 2.89 14.1
## 10 BH Low 511. 10 88.9 0 29.9 9.48
## # ℹ 266 more rows
## # ℹ 6 more variables: tmx_mean <dbl>, cwd_sem <dbl>, pck_sem <dbl>,
## # ppt_sem <dbl>, tmn_sem <dbl>, tmx_sem <dbl>pop_elev_climate_recent_avgs <- pop_elev_climate_recent_avgs %>% mutate(PckSum=sum(pck_mean)) #estimate of average total snowpack in a year
pop_elev_climate_recent_avgs %>% arrange(PckSum)## # A tibble: 276 × 15
## # Groups: parent.pop, elevation.group, elev_m [23]
## parent.pop elevation.group elev_m month cwd_mean pck_mean ppt_mean tmn_mean
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 1 29.3 0 125. 2.68
## 2 BH Low 511. 2 40.9 0 100. 3.43
## 3 BH Low 511. 3 53.8 0 85.4 4.90
## 4 BH Low 511. 4 59.0 0 47.2 6.37
## 5 BH Low 511. 5 51.5 0 23.1 9.76
## 6 BH Low 511. 6 87.1 0 7.43 13.4
## 7 BH Low 511. 7 136. 0 1.40 17.2
## 8 BH Low 511. 8 154. 0 0.507 16.8
## 9 BH Low 511. 9 130. 0 2.89 14.1
## 10 BH Low 511. 10 88.9 0 29.9 9.48
## # ℹ 266 more rows
## # ℹ 7 more variables: tmx_mean <dbl>, cwd_sem <dbl>, pck_sem <dbl>,
## # ppt_sem <dbl>, tmn_sem <dbl>, tmx_sem <dbl>, PckSum <dbl>#Exploratory filters
pop_elev_climate_recent %>% filter(parent.pop=="SC") %>% filter(year==2016 | year==2017) #snow pack in Jan 2017## # A tibble: 24 × 10
## parent.pop elevation.group elev_m year month cwd pck ppt tmn tmx
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 SC Low 422. 2016 1 20.2 0 2.12e+2 4.03 12.0
## 2 SC Low 422. 2016 2 34.6 0 3.34e+1 5.05 17.7
## 3 SC Low 422. 2016 3 30.6 0 2.23e+2 6.17 16.9
## 4 SC Low 422. 2016 4 37.5 0 7.73e+1 7.82 20.6
## 5 SC Low 422. 2016 5 69.0 0 1.22e+1 9.81 24.1
## 6 SC Low 422. 2016 6 94.8 0 0 13.2 31.8
## 7 SC Low 422. 2016 7 142. 0 0 14.6 33.8
## 8 SC Low 422. 2016 8 159. 0 3.00e-2 15.1 33.2
## 9 SC Low 422. 2016 9 126. 0 0 12.8 29.8
## 10 SC Low 422. 2016 10 80.1 0 1.83e+2 9.38 22.4
## # ℹ 14 more rowspop_elev_climate_recent %>% filter(parent.pop=="IH") %>% filter(pck >0) #snow pack in Jan 1993 & 2017 and Feb 2019## # A tibble: 4 × 10
## parent.pop elevation.group elev_m year month cwd pck ppt tmn tmx
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 IH Low 454. 1992 12 18.8 4.13 301. 0.860 11.0
## 2 IH Low 454. 1993 1 15.6 5.34 364. 1.72 11.2
## 3 IH Low 454. 2017 1 15.6 18.0 495. 2.31 10.2
## 4 IH Low 454. 2019 2 19.9 42.3 429. 1.60 9.46pop_elev_climate_recent %>% filter(parent.pop=="SQ3") %>% filter(pck==0, ppt < 10) #high elev pops get very little rain and high cwd during growth season ...## # A tibble: 106 × 10
## parent.pop elevation.group elev_m year month cwd pck ppt tmn tmx
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 SQ3 High 2373. 1992 6 95.0 0 7.54 4.84 17.6
## 2 SQ3 High 2373. 1992 8 82.0 0 7.80 10.7 23.8
## 3 SQ3 High 2373. 1992 9 103. 0 4.26 7.52 19.9
## 4 SQ3 High 2373. 1992 11 39.7 0 3.78 -1.58 8.87
## 5 SQ3 High 2373. 1993 7 88.3 0 0.0200 7.11 20.9
## 6 SQ3 High 2373. 1993 8 89.4 0 3.48 8.38 22.2
## 7 SQ3 High 2373. 1993 9 91.6 0 0.100 7.45 20.3
## 8 SQ3 High 2373. 1994 6 95.1 0 0.0100 6.79 19.9
## 9 SQ3 High 2373. 1994 7 114. 0 6.24 10.3 23.9
## 10 SQ3 High 2373. 1994 8 126. 0 0.300 11.8 24.4
## # ℹ 96 more rowsHistorical climate
pop_elev_climate_historical_avgs <- pop_elev_climate_historical %>% group_by(parent.pop, elevation.group, elev_m, month) %>% summarise_at(c("cwd", "pck", "ppt", "tmn", "tmx"), c(mean, sem), na.rm = TRUE)
names(pop_elev_climate_historical_avgs) <- gsub("fn2", "sem", colnames(pop_elev_climate_historical_avgs))
names(pop_elev_climate_historical_avgs) <-gsub("fn1", "mean", colnames(pop_elev_climate_historical_avgs))
pop_elev_climate_historical_avgs #30 year averages per month for each pop## # A tibble: 276 × 14
## # Groups: parent.pop, elevation.group, elev_m [23]
## parent.pop elevation.group elev_m month cwd_mean pck_mean ppt_mean tmn_mean
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 1 28.1 0.234 98.4 1.25
## 2 BH Low 511. 2 40.6 0 95.9 2.77
## 3 BH Low 511. 3 50.9 0 102. 3.76
## 4 BH Low 511. 4 61.0 0 51.0 5.44
## 5 BH Low 511. 5 61.1 0 13.0 8.64
## 6 BH Low 511. 6 89.3 0 4.71 12.3
## 7 BH Low 511. 7 127. 0 1.01 15.5
## 8 BH Low 511. 8 148. 0 1.82 15.1
## 9 BH Low 511. 9 127. 0 10.4 12.6
## 10 BH Low 511. 10 87.1 0 32.6 8.40
## # ℹ 266 more rows
## # ℹ 6 more variables: tmx_mean <dbl>, cwd_sem <dbl>, pck_sem <dbl>,
## # ppt_sem <dbl>, tmn_sem <dbl>, tmx_sem <dbl>pop_elev_climate_historical_avgs <- pop_elev_climate_historical_avgs %>% mutate(PckSum=sum(pck_mean)) #estimate of average total snowpack in a year
pop_elev_climate_historical_avgs %>% arrange(PckSum) #IH PckSum = 17...## # A tibble: 276 × 15
## # Groups: parent.pop, elevation.group, elev_m [23]
## parent.pop elevation.group elev_m month cwd_mean pck_mean ppt_mean tmn_mean
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 1 28.1 0.234 98.4 1.25
## 2 BH Low 511. 2 40.6 0 95.9 2.77
## 3 BH Low 511. 3 50.9 0 102. 3.76
## 4 BH Low 511. 4 61.0 0 51.0 5.44
## 5 BH Low 511. 5 61.1 0 13.0 8.64
## 6 BH Low 511. 6 89.3 0 4.71 12.3
## 7 BH Low 511. 7 127. 0 1.01 15.5
## 8 BH Low 511. 8 148. 0 1.82 15.1
## 9 BH Low 511. 9 127. 0 10.4 12.6
## 10 BH Low 511. 10 87.1 0 32.6 8.40
## # ℹ 266 more rows
## # ℹ 7 more variables: tmx_mean <dbl>, cwd_sem <dbl>, pck_sem <dbl>,
## # ppt_sem <dbl>, tmn_sem <dbl>, tmx_sem <dbl>, PckSum <dbl>#Exploratory filters
pop_elev_climate_historical %>% filter(parent.pop=="BH") %>% filter(pck >0) #snow pack in Jan 1982## # A tibble: 1 × 10
## parent.pop elevation.group elev_m year month cwd pck ppt tmn tmx
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 1982 1 24.3 7.02 203. 0.400 10.9pop_elev_climate_historical %>% filter(parent.pop=="SC") %>% filter(pck >0) #snow pack in Jan 1973 and 1982## # A tibble: 2 × 10
## parent.pop elevation.group elev_m year month cwd pck ppt tmn tmx
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 SC Low 422. 1973 1 19.1 2.94 273. 1.21 11.0
## 2 SC Low 422. 1982 1 18.6 15.0 209. 0.120 10.3pop_elev_climate_historical %>% filter(parent.pop=="CC") %>% filter(pck >0) #snow pack in Jan 1969 and 1973## # A tibble: 2 × 10
## parent.pop elevation.group elev_m year month cwd pck ppt tmn tmx
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CC Low 313 1969 1 16.7 21.4 415. 1.85 10.4
## 2 CC Low 313 1973 1 16.8 7.10 358. 1.68 10.9pop_elev_climate_historical %>% filter(parent.pop=="TM2") %>% filter(pck >0) #snow pack in Dec '68, Jan-Feb '69, Dec '71, Dec '72, Jan '73, Jan '82## # A tibble: 7 × 10
## parent.pop elevation.group elev_m year month cwd pck ppt tmn tmx
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TM2 Low 379. 1968 12 10.3 6.04 263. 0.930 10.1
## 2 TM2 Low 379. 1969 1 3.81 61.4 464. 1.49 10.0
## 3 TM2 Low 379. 1969 2 0 30.5 312. 2.34 10.2
## 4 TM2 Low 379. 1971 12 17.2 3.88 199. 0.340 9.62
## 5 TM2 Low 379. 1972 12 10.1 11.4 153. -0.390 8.63
## 6 TM2 Low 379. 1973 1 4.85 47.9 388. 1.27 10.4
## 7 TM2 Low 379. 1982 1 3.91 2.98 201. 0.800 9.66pop_elev_climate_historical %>% filter(parent.pop=="IH") %>% filter(pck >0) #snow pack in Dec '68, Jan-Feb '69, Dec '70, Dec '71, Jan & Dec '72, Jan-Feb '73, Jan '79## # A tibble: 11 × 10
## parent.pop elevation.group elev_m year month cwd pck ppt tmn
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 IH Low 454. 1968 12 16.5 17.8 230. -0.0800
## 2 IH Low 454. 1969 1 15.3 107. 529. 1.05
## 3 IH Low 454. 1969 2 19.7 129. 301. 1.14
## 4 IH Low 454. 1970 12 16.6 27.9 345. 1.58
## 5 IH Low 454. 1971 12 17.4 59.3 262. -0.370
## 6 IH Low 454. 1972 1 18.3 19.6 81.1 -0.0200
## 7 IH Low 454. 1972 12 17.1 20.4 143. -1.83
## 8 IH Low 454. 1973 1 14.9 105. 381. 0.0800
## 9 IH Low 454. 1973 2 21.6 0.480 247. 3.5
## 10 IH Low 454. 1979 1 17.4 4.82 227. 0.5
## 11 IH Low 454. 1982 1 14.9 29.3 229. -0.220
## # ℹ 1 more variable: tmx <dbl>Simpler version (similar results to above calcs)
##recent
pop_elev_climate_recent_sums <- pop_elev_climate_recent %>%
group_by(parent.pop, elevation.group, elev_m, year) %>%
summarise(PckSum=sum(pck))## `summarise()` has grouped output by 'parent.pop', 'elevation.group', 'elev_m'.
## You can override using the `.groups` argument.pop_elev_climate_recent_sums_avgs <- pop_elev_climate_recent_sums %>%
group_by(parent.pop, elevation.group, elev_m) %>%
summarise(meanPckSum=mean(PckSum), semPckSum=sem(PckSum)) %>%
arrange(meanPckSum)## `summarise()` has grouped output by 'parent.pop', 'elevation.group'. You can
## override using the `.groups` argument.pop_elev_climate_recent_sums_avgs## # A tibble: 23 × 5
## # Groups: parent.pop, elevation.group [23]
## parent.pop elevation.group elev_m meanPckSum semPckSum
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 BH Low 511. 0 0
## 2 CC Low 313 0 0
## 3 TM2 Low 379. 0 0
## 4 SC Low 422. 0.252 0.252
## 5 IH Low 454. 2.32 1.51
## 6 DPR Mid 1019. 107. 29.3
## 7 FR Mid 787 229. 45.8
## 8 WV Mid 749. 322. 51.7
## 9 WR Mid 1158 391. 71.7
## 10 SQ1 Mid 1921. 613. 97.5
## # ℹ 13 more rows##historical
pop_elev_climate_historical_sums <- pop_elev_climate_historical %>%
group_by(parent.pop, elevation.group, elev_m, year) %>%
summarise(PckSum=sum(pck))## `summarise()` has grouped output by 'parent.pop', 'elevation.group', 'elev_m'.
## You can override using the `.groups` argument.pop_elev_climate_historical_sums_avgs <- pop_elev_climate_historical_sums %>%
group_by(parent.pop, elevation.group, elev_m) %>%
summarise(meanPckSum=mean(PckSum), semPckSum=sem(PckSum)) %>%
arrange(meanPckSum)## `summarise()` has grouped output by 'parent.pop', 'elevation.group'. You can
## override using the `.groups` argument.pop_elev_climate_historical_sums_avgs## # A tibble: 23 × 5
## # Groups: parent.pop, elevation.group [23]
## parent.pop elevation.group elev_m meanPckSum semPckSum
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 BH Low 511. 0.234 0.234
## 2 SC Low 422. 0.597 0.506
## 3 CC Low 313 0.951 0.745
## 4 TM2 Low 379. 5.47 3.40
## 5 IH Low 454. 17.3 8.64
## 6 DPR Mid 1019. 228. 60.8
## 7 FR Mid 787 261. 62.3
## 8 WV Mid 749. 404. 83.5
## 9 WR Mid 1158 561. 110.
## 10 WL1 Mid 1614. 892. 134.
## # ℹ 13 more rowsFor each year separately
- Populations that get less than 70 mm (b/c that’s the avg height in Oct at WL2 garden) of snow pack in a year (on average): growth season = when there is no snowpack + significant rain event (> 10 mm) or lower cwd (< 88 or 86). CWD = 88 = 3rd quartile of the cwd_mean for these pops (recent climate). CWD = 86 = 3rd quartile of cwd_mean for historical climate
Jenny has used 25 mm as germinating inducing rain, 10 mm would probably be fine for sustaining (esp b/c it’s a sum of ppt for the whole month) Raise rain for germination and leave CWD is for just growing - John and Sam have code for daily data, with “first day to x event” - Ask Sam if she’s calculated this for STTO pops
Recent Climate
nosnow_pops_recent <- pop_elev_climate_recent_avgs %>% filter(PckSum < 70)
unique(nosnow_pops_recent$parent.pop) #BH, CC, TM2, SC, IH ## [1] "BH" "CC" "IH" "SC" "TM2"summary(nosnow_pops_recent) #3rd quartile of CWD = 88## parent.pop elevation.group elev_m month
## Length:60 Length:60 Min. :313.0 Min. : 1.00
## Class :character Class :character 1st Qu.:379.2 1st Qu.: 3.75
## Mode :character Mode :character Median :421.5 Median : 6.50
## Mean :415.8 Mean : 6.50
## 3rd Qu.:454.1 3rd Qu.: 9.25
## Max. :511.4 Max. :12.00
## cwd_mean pck_mean ppt_mean tmn_mean
## Min. : 12.95 Min. :0.00000 Min. : 0.1587 Min. : 2.366
## 1st Qu.: 31.01 1st Qu.:0.00000 1st Qu.: 8.9782 1st Qu.: 4.874
## Median : 49.74 Median :0.00000 Median : 59.4208 Median : 8.649
## Mean : 62.04 Mean :0.04294 Mean : 77.1655 Mean : 9.172
## 3rd Qu.: 87.57 3rd Qu.:0.00000 3rd Qu.:127.8994 3rd Qu.:13.809
## Max. :167.33 Max. :1.41000 Max. :231.6770 Max. :17.510
## tmx_mean cwd_sem pck_sem ppt_sem
## Min. :12.37 Min. :0.1709 Min. :0.00000 Min. : 0.07712
## 1st Qu.:16.29 1st Qu.:0.5503 1st Qu.:0.00000 1st Qu.: 2.49284
## Median :22.41 Median :1.6062 Median :0.00000 Median :10.03852
## Mean :22.91 Mean :2.4735 Mean :0.04031 Mean :10.89493
## 3rd Qu.:30.58 3rd Qu.:4.0240 3rd Qu.:0.00000 3rd Qu.:17.03939
## Max. :35.05 Max. :9.9766 Max. :1.41000 Max. :27.98985
## tmn_sem tmx_sem PckSum
## Min. :0.1835 Min. :0.1742 Min. :0.0000
## 1st Qu.:0.2267 1st Qu.:0.2637 1st Qu.:0.0000
## Median :0.2440 Median :0.3399 Median :0.0000
## Mean :0.2416 Mean :0.3280 Mean :0.5153
## 3rd Qu.:0.2660 3rd Qu.:0.3717 3rd Qu.:0.2523
## Max. :0.2791 Max. :0.4856 Max. :2.3243nosnow_pops_recent_tojoin <- nosnow_pops_recent %>% select(parent.pop:elev_m, PckSum) %>% distinct()
nosnow_pops_recent_years <- left_join(nosnow_pops_recent_tojoin, pop_elev_climate_recent) ## Joining with `by = join_by(parent.pop, elevation.group, elev_m)`nosnow_pops_recent_years %>% filter(month=="7", ppt>=25) #never get >=25mm of ppt in July so can start with Aug## # A tibble: 1 × 11
## # Groups: parent.pop, elevation.group, elev_m [1]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt tmn
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 0 1992 7 82.4 0 33.7 16.0
## # ℹ 1 more variable: tmx <dbl>nosnow_pops_recent_years %>% filter(month=="8", ppt>=25)## # A tibble: 3 × 11
## # Groups: parent.pop, elevation.group, elev_m [3]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt tmn
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CC Low 313 0 2003 8 71.8 0 25.1 16.2
## 2 IH Low 454. 2.32 2003 8 54.7 0 28.8 15.0
## 3 TM2 Low 379. 0 1997 8 139. 0 25.8 15.5
## # ℹ 1 more variable: tmx <dbl>growyear_months <- tibble(month=c(1:12), growmonth=c(6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5))
nosnow_recent_growyear_months <- full_join(growyear_months, nosnow_pops_recent_years)## Joining with `by = join_by(month)`dim(nosnow_recent_growyear_months)## [1] 1800 12#nosnow_recent_growyear_months %>% filter(ppt==0, cwd<88)
nosnow_first_month <- nosnow_recent_growyear_months %>%
group_by(parent.pop, elevation.group, elev_m, year) %>%
filter(ppt>=25) %>%
arrange(growmonth) %>%
filter(row_number()==1) #get first month for each pop and year with germinating inducing rain
#nosnow_first_month %>% filter(parent.pop=="IH") %>% arrange(year)
nosnow_first_month_tomerge <- nosnow_first_month %>%
select(parent.pop:elev_m, year, firstmonth=growmonth)
nosnow_first_month_col <- full_join(nosnow_recent_growyear_months, nosnow_first_month_tomerge)## Joining with `by = join_by(parent.pop, elevation.group, elev_m, year)`dim(nosnow_first_month_col)## [1] 1800 13nosnow_last_month <- nosnow_recent_growyear_months %>%
group_by(parent.pop, elevation.group, elev_m, year) %>%
filter(cwd>88) %>%
arrange(month) %>%
filter(row_number()==1)
nosnow_last_month_tomerge <- nosnow_last_month %>%
select(parent.pop:elev_m, year, lastmonth=growmonth) #last month is in grow month not calendar month format
nosnow_last_month_col <- full_join(nosnow_first_month_col, nosnow_last_month_tomerge)## Joining with `by = join_by(parent.pop, elevation.group, elev_m, year)`dim(nosnow_last_month_col)## [1] 1800 14#Checking for weird cases
nosnow_last_month_col %>% filter(is.na(lastmonth)) %>% arrange(year) #3 years when IH doesn't have a last month based on these criteria (cwd never goes above 88 in those years)## # A tibble: 36 × 14
## month growmonth parent.pop elevation.group elev_m PckSum year cwd pck
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 6 IH Low 454. 2.32 1998 16.3 0
## 2 2 7 IH Low 454. 2.32 1998 21.0 0
## 3 3 8 IH Low 454. 2.32 1998 30.3 0
## 4 4 9 IH Low 454. 2.32 1998 21.1 0
## 5 5 10 IH Low 454. 2.32 1998 0 0
## 6 6 11 IH Low 454. 2.32 1998 0 0
## 7 7 12 IH Low 454. 2.32 1998 42.5 0
## 8 8 1 IH Low 454. 2.32 1998 83.1 0
## 9 9 2 IH Low 454. 2.32 1998 87.6 0
## 10 10 3 IH Low 454. 2.32 1998 68.9 0
## # ℹ 26 more rows
## # ℹ 5 more variables: ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_last_month_col %>% filter(is.na(firstmonth)) %>% arrange(year) #1 year when BH doesn't have a first month based on these criteria (ppt never above 25)## # A tibble: 12 × 14
## month growmonth parent.pop elevation.group elev_m PckSum year cwd pck
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 6 BH Low 511. 0 2013 28.4 0
## 2 2 7 BH Low 511. 0 2013 40.4 0
## 3 3 8 BH Low 511. 0 2013 61.2 0
## 4 4 9 BH Low 511. 0 2013 74.9 0
## 5 5 10 BH Low 511. 0 2013 75.7 0
## 6 6 11 BH Low 511. 0 2013 101. 0
## 7 7 12 BH Low 511. 0 2013 136. 0
## 8 8 1 BH Low 511. 0 2013 150. 0
## 9 9 2 BH Low 511. 0 2013 129. 0
## 10 10 3 BH Low 511. 0 2013 86.8 0
## 11 11 4 BH Low 511. 0 2013 47.2 0
## 12 12 5 BH Low 511. 0 2013 31.3 0
## # ℹ 5 more variables: ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_last_month_col %>% filter(lastmonth<5) %>% arrange(parent.pop, year) #some cases where last month is less than 5 (earlier than December)## # A tibble: 444 × 14
## month growmonth parent.pop elevation.group elev_m PckSum year cwd pck
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 6 BH Low 511. 0 1995 27.9 0
## 2 2 7 BH Low 511. 0 1995 43.9 0
## 3 3 8 BH Low 511. 0 1995 41.1 0
## 4 4 9 BH Low 511. 0 1995 38.9 0
## 5 5 10 BH Low 511. 0 1995 12.3 0
## 6 6 11 BH Low 511. 0 1995 34.4 0
## 7 7 12 BH Low 511. 0 1995 80.9 0
## 8 8 1 BH Low 511. 0 1995 180. 0
## 9 9 2 BH Low 511. 0 1995 134. 0
## 10 10 3 BH Low 511. 0 1995 92.1 0
## # ℹ 434 more rows
## # ℹ 5 more variables: ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_last_month_col %>% filter(lastmonth<firstmonth, lastmonth<5) %>% arrange(parent.pop, year) #most of the above are when the last month is before the first month (in growyear, not calendar year)## # A tibble: 432 × 14
## month growmonth parent.pop elevation.group elev_m PckSum year cwd pck
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 6 BH Low 511. 0 1995 27.9 0
## 2 2 7 BH Low 511. 0 1995 43.9 0
## 3 3 8 BH Low 511. 0 1995 41.1 0
## 4 4 9 BH Low 511. 0 1995 38.9 0
## 5 5 10 BH Low 511. 0 1995 12.3 0
## 6 6 11 BH Low 511. 0 1995 34.4 0
## 7 7 12 BH Low 511. 0 1995 80.9 0
## 8 8 1 BH Low 511. 0 1995 180. 0
## 9 9 2 BH Low 511. 0 1995 134. 0
## 10 10 3 BH Low 511. 0 1995 92.1 0
## # ℹ 422 more rows
## # ℹ 5 more variables: ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_last_month_col %>% filter(lastmonth>firstmonth & lastmonth<5) %>% arrange(parent.pop, year) #IH 2003, first month = 1 (Aug) & last month = 2 (Sept). Seems like there was a fluke rain event in Aug, but rain picked up again in November - CONSIDER MANUALLY ADJUSTING THE GROWTH SEASON FOR THIS CASE## # A tibble: 12 × 14
## month growmonth parent.pop elevation.group elev_m PckSum year cwd pck
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 6 IH Low 454. 2.32 2003 20.9 0
## 2 2 7 IH Low 454. 2.32 2003 28.0 0
## 3 3 8 IH Low 454. 2.32 2003 41.6 0
## 4 4 9 IH Low 454. 2.32 2003 20.4 0
## 5 5 10 IH Low 454. 2.32 2003 0 0
## 6 6 11 IH Low 454. 2.32 2003 57.2 0
## 7 7 12 IH Low 454. 2.32 2003 74.5 0
## 8 8 1 IH Low 454. 2.32 2003 54.7 0
## 9 9 2 IH Low 454. 2.32 2003 130. 0
## 10 10 3 IH Low 454. 2.32 2003 83.1 0
## 11 11 4 IH Low 454. 2.32 2003 33.7 0
## 12 12 5 IH Low 454. 2.32 2003 19.8 0
## # ℹ 5 more variables: ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_last_month_col %>% filter(lastmonth==5) %>% arrange(parent.pop, year) #0 cases where 5 is the last month ## # A tibble: 0 × 14
## # ℹ 14 variables: month <dbl>, growmonth <dbl>, parent.pop <chr>,
## # elevation.group <chr>, elev_m <dbl>, PckSum <dbl>, year <chr>, cwd <dbl>,
## # pck <dbl>, ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_last_month_col %>% filter(lastmonth==firstmonth) %>% arrange(parent.pop, year) #first month and last month are never the same ## # A tibble: 0 × 14
## # ℹ 14 variables: month <dbl>, growmonth <dbl>, parent.pop <chr>,
## # elevation.group <chr>, elev_m <dbl>, PckSum <dbl>, year <chr>, cwd <dbl>,
## # pck <dbl>, ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_last_month_col %>% filter(growmonth==firstmonth+1, cwd>88) %>% arrange(parent.pop, year) #2 cases where cwd is high in the second growth month ## # A tibble: 2 × 14
## month growmonth parent.pop elevation.group elev_m PckSum year cwd pck
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 9 2 CC Low 313 0 2003 107. 0
## 2 9 2 IH Low 454. 2.32 2003 130. 0
## # ℹ 5 more variables: ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_grwseason_recent <- nosnow_last_month_col %>% #fill in all the months b/t the first and last for the full growth season
group_by(parent.pop, elevation.group, elev_m, year) %>%
filter(growmonth>=firstmonth) %>% #first and last month are in grow month format not calendar year
filter(ifelse(lastmonth<5, growmonth<=12, growmonth<lastmonth))
summary(nosnow_grwseason_recent) ## month growmonth parent.pop elevation.group
## Min. : 1.00 Min. : 1.000 Length:1215 Length:1215
## 1st Qu.: 3.00 1st Qu.: 5.000 Class :character Class :character
## Median : 5.00 Median : 7.000 Mode :character Mode :character
## Mean : 5.86 Mean : 7.018
## 3rd Qu.:10.00 3rd Qu.: 9.000
## Max. :12.00 Max. :12.000
## elev_m PckSum year cwd
## Min. :313.0 Min. :0.0000 Length:1215 Min. : 0.00
## 1st Qu.:379.2 1st Qu.:0.0000 Class :character 1st Qu.: 21.06
## Median :421.5 Median :0.0000 Mode :character Median : 32.59
## Mean :414.2 Mean :0.5621 Mean : 38.14
## 3rd Qu.:454.1 3rd Qu.:0.2523 3rd Qu.: 52.62
## Max. :511.4 Max. :2.3243 Max. :139.30
## pck ppt tmn tmx
## Min. : 0.00000 Min. : 0.00 Min. :-1.050 Min. : 9.38
## 1st Qu.: 0.00000 1st Qu.: 29.30 1st Qu.: 3.935 1st Qu.:14.16
## Median : 0.00000 Median : 74.92 Median : 5.890 Median :17.82
## Mean : 0.02881 Mean :109.07 Mean : 6.722 Mean :19.03
## 3rd Qu.: 0.00000 3rd Qu.:152.41 3rd Qu.: 9.005 3rd Qu.:22.96
## Max. :17.96000 Max. :614.04 Max. :19.740 Max. :35.97
## firstmonth lastmonth
## Min. :1.000 Min. : 1.000
## 1st Qu.:3.000 1st Qu.: 2.000
## Median :3.000 Median :11.000
## Mean :3.267 Mean : 8.278
## 3rd Qu.:4.000 3rd Qu.:12.000
## Max. :5.000 Max. :12.000nosnow_grwseason_recent %>% filter(cwd > 88) %>% filter(growmonth==firstmonth) #some cases when cwd is high in the first month (when ppt >25)## # A tibble: 9 × 14
## # Groups: parent.pop, elevation.group, elev_m, year [9]
## month growmonth parent.pop elevation.group elev_m PckSum year cwd pck
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 8 1 TM2 Low 379. 0 1997 139. 0
## 2 9 2 IH Low 454. 2.32 2000 115. 0
## 3 9 2 IH Low 454. 2.32 2013 121. 0
## 4 9 2 IH Low 454. 2.32 2014 112. 0
## 5 9 2 SC Low 422. 0.252 2019 119. 0
## 6 10 3 BH Low 511. 0 1992 91.0 0
## 7 10 3 BH Low 511. 0 1996 88.5 0
## 8 10 3 BH Low 511. 0 2011 89.2 0
## 9 10 3 BH Low 511. 0 2021 89.1 0
## # ℹ 5 more variables: ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>xtabs(~parent.pop+month, data=nosnow_grwseason_recent)## month
## parent.pop 1 2 3 4 5 6 7 8 9 10 11 12
## BH 29 29 29 29 29 8 1 0 0 10 26 29
## CC 30 30 30 30 30 12 2 1 3 23 29 30
## IH 27 27 27 27 27 27 27 1 4 21 26 27
## SC 30 30 30 30 30 13 0 0 1 15 29 30
## TM2 30 30 30 30 17 12 7 1 3 21 29 30options(max.print=1000000)
xtabs(~year+month+parent.pop, data=nosnow_grwseason_recent)## , , parent.pop = BH
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1992 1 1 1 1 1 0 0 0 0 1 1 1
## 1993 1 1 1 1 1 1 0 0 0 0 1 1
## 1994 1 1 1 1 1 0 0 0 0 1 1 1
## 1995 1 1 1 1 1 1 1 0 0 0 0 1
## 1996 1 1 1 1 1 1 0 0 0 1 1 1
## 1997 1 1 1 1 1 0 0 0 0 0 1 1
## 1998 1 1 1 1 1 1 0 0 0 0 1 1
## 1999 1 1 1 1 1 0 0 0 0 0 1 1
## 2000 1 1 1 1 1 1 0 0 0 1 1 1
## 2001 1 1 1 1 1 0 0 0 0 0 1 1
## 2002 1 1 1 1 1 0 0 0 0 0 1 1
## 2003 1 1 1 1 1 0 0 0 0 0 1 1
## 2004 1 1 1 1 1 0 0 0 0 1 1 1
## 2005 1 1 1 1 1 0 0 0 0 0 0 1
## 2006 1 1 1 1 1 0 0 0 0 0 1 1
## 2007 1 1 1 1 1 0 0 0 0 0 0 1
## 2008 1 1 1 1 1 0 0 0 0 0 1 1
## 2009 1 1 1 1 1 1 0 0 0 1 1 1
## 2010 1 1 1 1 1 0 0 0 0 1 1 1
## 2011 1 1 1 1 1 1 0 0 0 1 1 1
## 2012 1 1 1 1 1 0 0 0 0 0 1 1
## 2013 0 0 0 0 0 0 0 0 0 0 0 0
## 2014 1 1 1 1 1 0 0 0 0 0 1 1
## 2015 1 1 1 1 1 0 0 0 0 0 1 1
## 2016 1 1 1 1 1 0 0 0 0 1 1 1
## 2017 1 1 1 1 1 1 0 0 0 0 1 1
## 2018 1 1 1 1 1 0 0 0 0 0 1 1
## 2019 1 1 1 1 1 0 0 0 0 0 1 1
## 2020 1 1 1 1 1 0 0 0 0 0 1 1
## 2021 1 1 1 1 1 0 0 0 0 1 1 1
##
## , , parent.pop = CC
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1992 1 1 1 1 1 1 0 0 0 1 1 1
## 1993 1 1 1 1 1 1 0 0 0 1 1 1
## 1994 1 1 1 1 1 0 0 0 0 1 1 1
## 1995 1 1 1 1 1 1 1 0 0 0 0 1
## 1996 1 1 1 1 1 0 0 0 0 1 1 1
## 1997 1 1 1 1 1 1 1 0 0 1 1 1
## 1998 1 1 1 1 1 1 0 0 0 1 1 1
## 1999 1 1 1 1 1 1 0 0 0 1 1 1
## 2000 1 1 1 1 1 0 0 0 0 1 1 1
## 2001 1 1 1 1 1 0 0 0 0 1 1 1
## 2002 1 1 1 1 1 0 0 0 0 0 1 1
## 2003 1 1 1 1 1 0 0 1 1 1 1 1
## 2004 1 1 1 1 1 0 0 0 0 1 1 1
## 2005 1 1 1 1 1 1 0 0 0 1 1 1
## 2006 1 1 1 1 1 0 0 0 0 0 1 1
## 2007 1 1 1 1 1 0 0 0 0 1 1 1
## 2008 1 1 1 1 1 0 0 0 0 1 1 1
## 2009 1 1 1 1 1 1 0 0 0 1 1 1
## 2010 1 1 1 1 1 0 0 0 0 1 1 1
## 2011 1 1 1 1 1 1 0 0 0 1 1 1
## 2012 1 1 1 1 1 1 0 0 0 1 1 1
## 2013 1 1 1 1 1 1 0 0 1 1 1 1
## 2014 1 1 1 1 1 0 0 0 0 1 1 1
## 2015 1 1 1 1 1 0 0 0 0 0 1 1
## 2016 1 1 1 1 1 0 0 0 0 1 1 1
## 2017 1 1 1 1 1 1 0 0 0 0 1 1
## 2018 1 1 1 1 1 0 0 0 0 0 1 1
## 2019 1 1 1 1 1 0 0 0 1 1 1 1
## 2020 1 1 1 1 1 0 0 0 0 0 1 1
## 2021 1 1 1 1 1 0 0 0 0 1 1 1
##
## , , parent.pop = IH
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1992 1 1 1 1 1 1 1 0 0 1 1 1
## 1993 1 1 1 1 1 1 1 0 0 1 1 1
## 1994 1 1 1 1 1 1 1 0 0 1 1 1
## 1995 1 1 1 1 1 1 1 0 0 0 0 1
## 1996 1 1 1 1 1 1 1 0 0 1 1 1
## 1997 1 1 1 1 1 1 1 0 0 1 1 1
## 1998 0 0 0 0 0 0 0 0 0 0 0 0
## 1999 1 1 1 1 1 1 1 0 0 1 1 1
## 2000 1 1 1 1 1 1 1 0 1 1 1 1
## 2001 1 1 1 1 1 1 1 0 0 1 1 1
## 2002 1 1 1 1 1 1 1 0 0 0 1 1
## 2003 1 1 1 1 1 1 1 1 1 1 1 1
## 2004 1 1 1 1 1 1 1 0 0 1 1 1
## 2005 0 0 0 0 0 0 0 0 0 0 0 0
## 2006 1 1 1 1 1 1 1 0 0 0 1 1
## 2007 1 1 1 1 1 1 1 0 0 1 1 1
## 2008 1 1 1 1 1 1 1 0 0 1 1 1
## 2009 1 1 1 1 1 1 1 0 0 1 1 1
## 2010 1 1 1 1 1 1 1 0 0 1 1 1
## 2011 1 1 1 1 1 1 1 0 0 1 1 1
## 2012 1 1 1 1 1 1 1 0 0 1 1 1
## 2013 1 1 1 1 1 1 1 0 1 1 1 1
## 2014 1 1 1 1 1 1 1 0 1 1 1 1
## 2015 1 1 1 1 1 1 1 0 0 0 1 1
## 2016 1 1 1 1 1 1 1 0 0 1 1 1
## 2017 1 1 1 1 1 1 1 0 0 0 1 1
## 2018 1 1 1 1 1 1 1 0 0 1 1 1
## 2019 0 0 0 0 0 0 0 0 0 0 0 0
## 2020 1 1 1 1 1 1 1 0 0 0 1 1
## 2021 1 1 1 1 1 1 1 0 0 1 1 1
##
## , , parent.pop = SC
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1992 1 1 1 1 1 1 0 0 0 1 1 1
## 1993 1 1 1 1 1 1 0 0 0 0 1 1
## 1994 1 1 1 1 1 0 0 0 0 1 1 1
## 1995 1 1 1 1 1 1 0 0 0 0 0 1
## 1996 1 1 1 1 1 1 0 0 0 1 1 1
## 1997 1 1 1 1 1 1 0 0 0 1 1 1
## 1998 1 1 1 1 1 1 0 0 0 0 1 1
## 1999 1 1 1 1 1 0 0 0 0 0 1 1
## 2000 1 1 1 1 1 1 0 0 0 1 1 1
## 2001 1 1 1 1 1 0 0 0 0 0 1 1
## 2002 1 1 1 1 1 0 0 0 0 0 1 1
## 2003 1 1 1 1 1 0 0 0 0 0 1 1
## 2004 1 1 1 1 1 0 0 0 0 1 1 1
## 2005 1 1 1 1 1 1 0 0 0 0 1 1
## 2006 1 1 1 1 1 0 0 0 0 0 1 1
## 2007 1 1 1 1 1 0 0 0 0 1 1 1
## 2008 1 1 1 1 1 0 0 0 0 0 1 1
## 2009 1 1 1 1 1 0 0 0 0 1 1 1
## 2010 1 1 1 1 1 0 0 0 0 1 1 1
## 2011 1 1 1 1 1 1 0 0 0 1 1 1
## 2012 1 1 1 1 1 1 0 0 0 1 1 1
## 2013 1 1 1 1 1 1 0 0 0 0 1 1
## 2014 1 1 1 1 1 0 0 0 0 0 1 1
## 2015 1 1 1 1 1 0 0 0 0 1 1 1
## 2016 1 1 1 1 1 0 0 0 0 1 1 1
## 2017 1 1 1 1 1 1 0 0 0 0 1 1
## 2018 1 1 1 1 1 0 0 0 0 0 1 1
## 2019 1 1 1 1 1 1 0 0 1 1 1 1
## 2020 1 1 1 1 1 0 0 0 0 0 1 1
## 2021 1 1 1 1 1 0 0 0 0 1 1 1
##
## , , parent.pop = TM2
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1992 1 1 1 1 0 0 0 0 0 1 1 1
## 1993 1 1 1 1 1 1 1 0 0 1 1 1
## 1994 1 1 1 1 1 0 0 0 0 0 1 1
## 1995 1 1 1 1 1 1 1 0 0 0 0 1
## 1996 1 1 1 1 1 1 1 0 0 1 1 1
## 1997 1 1 1 1 0 0 0 1 1 1 1 1
## 1998 1 1 1 1 1 1 1 0 0 1 1 1
## 1999 1 1 1 1 0 0 0 0 0 1 1 1
## 2000 1 1 1 1 1 0 0 0 0 1 1 1
## 2001 1 1 1 1 0 0 0 0 0 1 1 1
## 2002 1 1 1 1 0 0 0 0 0 0 1 1
## 2003 1 1 1 1 1 1 0 0 0 0 1 1
## 2004 1 1 1 1 0 0 0 0 0 1 1 1
## 2005 1 1 1 1 1 1 1 0 0 1 1 1
## 2006 1 1 1 1 1 1 0 0 0 0 1 1
## 2007 1 1 1 1 0 0 0 0 0 1 1 1
## 2008 1 1 1 1 0 0 0 0 0 1 1 1
## 2009 1 1 1 1 1 1 0 0 0 1 1 1
## 2010 1 1 1 1 1 1 0 0 0 1 1 1
## 2011 1 1 1 1 1 1 1 0 0 1 1 1
## 2012 1 1 1 1 1 0 0 0 0 1 1 1
## 2013 1 1 1 1 0 0 0 0 1 1 1 1
## 2014 1 1 1 1 0 0 0 0 0 1 1 1
## 2015 1 1 1 1 0 0 0 0 0 0 1 1
## 2016 1 1 1 1 0 0 0 0 0 1 1 1
## 2017 1 1 1 1 1 1 1 0 0 0 1 1
## 2018 1 1 1 1 1 0 0 0 0 0 1 1
## 2019 1 1 1 1 1 1 0 0 1 1 1 1
## 2020 1 1 1 1 1 0 0 0 0 0 1 1
## 2021 1 1 1 1 0 0 0 0 0 1 1 1nosnow_grwseason_recent %>% ggplot(aes(x=month)) + geom_histogram() +
scale_x_continuous(breaks=c(1, 2, 3, 4, 5,6,7,8,9,10, 11, 12)) +
facet_wrap(~parent.pop)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Apriori Assumption for growth season: Low: Oct-July
Historical Climate
nosnow_pops_historical <- pop_elev_climate_historical_avgs %>% filter(PckSum < 70)
unique(nosnow_pops_historical$parent.pop)## [1] "BH" "CC" "IH" "SC" "TM2"summary(nosnow_pops_historical) #3rd quartile of CWD = 87 ## parent.pop elevation.group elev_m month
## Length:60 Length:60 Min. :313.0 Min. : 1.00
## Class :character Class :character 1st Qu.:379.2 1st Qu.: 3.75
## Mode :character Mode :character Median :421.5 Median : 6.50
## Mean :415.8 Mean : 6.50
## 3rd Qu.:454.1 3rd Qu.: 9.25
## Max. :511.4 Max. :12.00
## cwd_mean pck_mean ppt_mean tmn_mean
## Min. : 12.15 Min. :0.0000 Min. : 1.012 Min. : 1.218
## 1st Qu.: 29.27 1st Qu.:0.0000 1st Qu.: 11.992 1st Qu.: 3.735
## Median : 55.90 Median :0.0000 Median : 67.216 Median : 7.683
## Mean : 62.17 Mean :0.4099 Mean : 74.659 Mean : 8.061
## 3rd Qu.: 86.85 3rd Qu.:0.0000 3rd Qu.:133.044 3rd Qu.:12.579
## Max. :163.60 Max. :8.8330 Max. :201.882 Max. :16.356
## tmx_mean cwd_sem pck_sem ppt_sem
## Min. :12.27 Min. : 0.1859 Min. :0.0000 Min. : 0.5719
## 1st Qu.:15.66 1st Qu.: 0.6581 1st Qu.:0.0000 1st Qu.: 2.7875
## Median :22.19 Median : 1.7177 Median :0.0000 Median :10.5243
## Mean :22.56 Mean : 2.4974 Mean :0.2836 Mean :10.8379
## 3rd Qu.:30.34 3rd Qu.: 3.8894 3rd Qu.:0.0000 3rd Qu.:17.4984
## Max. :34.69 Max. :10.6738 Max. :4.9412 Max. :24.5578
## tmn_sem tmx_sem PckSum
## Min. :0.1724 Min. :0.2166 Min. : 0.2340
## 1st Qu.:0.2110 1st Qu.:0.2944 1st Qu.: 0.5973
## Median :0.2372 Median :0.3474 Median : 0.9510
## Mean :0.2447 Mean :0.3499 Mean : 4.9187
## 3rd Qu.:0.2866 3rd Qu.:0.3745 3rd Qu.: 5.4673
## Max. :0.3384 Max. :0.5565 Max. :17.3440nosnow_pops_historical_tojoin <- nosnow_pops_historical %>% select(parent.pop:elev_m, PckSum) %>% distinct()
nosnow_pops_historical_years <- left_join(nosnow_pops_historical_tojoin, pop_elev_climate_historical) ## Joining with `by = join_by(parent.pop, elevation.group, elev_m)`nosnow_pops_historical_years %>% filter(month=="7", ppt>=25) #in 1974, ppt>25 for 4 pops in July...## # A tibble: 4 × 11
## # Groups: parent.pop, elevation.group, elev_m [4]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt tmn
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CC Low 313 0.951 1974 7 52.4 0 58.2 16.3
## 2 IH Low 454. 17.3 1974 7 0 0 79.3 15.4
## 3 SC Low 422. 0.597 1974 7 127. 0 61.4 14.8
## 4 TM2 Low 379. 5.47 1974 7 0 0 59.1 15.9
## # ℹ 1 more variable: tmx <dbl>nosnow_pops_historical_years %>% filter(year==1974) %>% arrange(parent.pop) #cwd high and ppt low in following months ## # A tibble: 60 × 11
## # Groups: parent.pop, elevation.group, elev_m [5]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 0.234 1974 1 27.6 0 91.6
## 2 BH Low 511. 0.234 1974 2 40.5 0 28.2
## 3 BH Low 511. 0.234 1974 3 49.0 0 129.
## 4 BH Low 511. 0.234 1974 4 38.6 0 74.2
## 5 BH Low 511. 0.234 1974 5 73.0 0 0
## 6 BH Low 511. 0.234 1974 6 97.2 0 0.0300
## 7 BH Low 511. 0.234 1974 7 68.5 0 17.0
## 8 BH Low 511. 0.234 1974 8 150. 0 0
## 9 BH Low 511. 0.234 1974 9 132. 0 0
## 10 BH Low 511. 0.234 1974 10 89.7 0 57.4
## # ℹ 50 more rows
## # ℹ 2 more variables: tmn <dbl>, tmx <dbl>nosnow_pops_historical_years %>% filter(month=="8", ppt>=25) %>% arrange(year, parent.pop) #11 cases of ppt>25 in Aug ## # A tibble: 11 × 11
## # Groups: parent.pop, elevation.group, elev_m [5]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt tmn
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CC Low 313 0.951 1965 8 70.3 0 38.7 16.6
## 2 TM2 Low 379. 5.47 1965 8 156. 0 39.8 16.3
## 3 CC Low 313 0.951 1968 8 69.3 0 28.1 14.2
## 4 TM2 Low 379. 5.47 1968 8 151. 0 28.9 13.8
## 5 IH Low 454. 17.3 1975 8 94.4 0 28.8 14.0
## 6 SC Low 422. 0.597 1975 8 169. 0 26.9 13.4
## 7 BH Low 511. 0.234 1976 8 168. 0 26.4 13.2
## 8 CC Low 313 0.951 1976 8 78.0 0 37.8 13.8
## 9 IH Low 454. 17.3 1976 8 152. 0 50.7 12.5
## 10 SC Low 422. 0.597 1976 8 148. 0 40.5 12.7
## 11 TM2 Low 379. 5.47 1976 8 150. 0 38.3 13.4
## # ℹ 1 more variable: tmx <dbl>nosnow_pops_historical_years %>% filter(year==1965) %>% arrange(parent.pop)## # A tibble: 60 × 11
## # Groups: parent.pop, elevation.group, elev_m [5]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 0.234 1965 1 27.4 0 88.1
## 2 BH Low 511. 0.234 1965 2 41.0 0 25.9
## 3 BH Low 511. 0.234 1965 3 57.2 0 50.6
## 4 BH Low 511. 0.234 1965 4 39.2 0 104.
## 5 BH Low 511. 0.234 1965 5 70.8 0 0.0500
## 6 BH Low 511. 0.234 1965 6 91.2 0 0
## 7 BH Low 511. 0.234 1965 7 119. 0 0
## 8 BH Low 511. 0.234 1965 8 99.9 0 8.87
## 9 BH Low 511. 0.234 1965 9 119. 0 0
## 10 BH Low 511. 0.234 1965 10 90.1 0 10.5
## # ℹ 50 more rows
## # ℹ 2 more variables: tmn <dbl>, tmx <dbl>nosnow_pops_historical_years %>% filter(year==1968) %>% arrange(parent.pop) ## # A tibble: 60 × 11
## # Groups: parent.pop, elevation.group, elev_m [5]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt tmn
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 0.234 1968 1 28.7 0 58.5 -0.350
## 2 BH Low 511. 0.234 1968 2 44.7 0 68.2 5.55
## 3 BH Low 511. 0.234 1968 3 59.1 0 62.1 4.02
## 4 BH Low 511. 0.234 1968 4 72.2 0 19.2 4.45
## 5 BH Low 511. 0.234 1968 5 73.7 0 20.7 7.90
## 6 BH Low 511. 0.234 1968 6 101. 0 0 13.1
## 7 BH Low 511. 0.234 1968 7 135. 0 0 16.4
## 8 BH Low 511. 0.234 1968 8 149. 0 0 13.8
## 9 BH Low 511. 0.234 1968 9 129. 0 0 12.1
## 10 BH Low 511. 0.234 1968 10 87.5 0 42.3 8.06
## # ℹ 50 more rows
## # ℹ 1 more variable: tmx <dbl>nosnow_pops_historical_years %>% filter(year==1975) %>% arrange(parent.pop) ## # A tibble: 60 × 11
## # Groups: parent.pop, elevation.group, elev_m [5]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 0.234 1975 1 28.3 0 43.9
## 2 BH Low 511. 0.234 1975 2 37.2 0 169.
## 3 BH Low 511. 0.234 1975 3 39.2 0 145.
## 4 BH Low 511. 0.234 1975 4 35.9 0 67.9
## 5 BH Low 511. 0.234 1975 5 72.4 0 2.73
## 6 BH Low 511. 0.234 1975 6 95.4 0 0.770
## 7 BH Low 511. 0.234 1975 7 121. 0 0.0300
## 8 BH Low 511. 0.234 1975 8 103. 0 8.65
## 9 BH Low 511. 0.234 1975 9 130. 0 3.14
## 10 BH Low 511. 0.234 1975 10 84.1 0 67.7
## # ℹ 50 more rows
## # ℹ 2 more variables: tmn <dbl>, tmx <dbl>nosnow_pops_historical_years %>% filter(year==1976) %>% arrange(parent.pop)## # A tibble: 60 × 11
## # Groups: parent.pop, elevation.group, elev_m [5]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 0.234 1976 1 29.4 0 7.71
## 2 BH Low 511. 0.234 1976 2 42.6 0 92.6
## 3 BH Low 511. 0.234 1976 3 57.1 0 39.8
## 4 BH Low 511. 0.234 1976 4 70.1 0 40.5
## 5 BH Low 511. 0.234 1976 5 79.7 0 0.240
## 6 BH Low 511. 0.234 1976 6 112. 0 0.210
## 7 BH Low 511. 0.234 1976 7 166. 0 0.790
## 8 BH Low 511. 0.234 1976 8 168. 0 26.4
## 9 BH Low 511. 0.234 1976 9 115. 0 32.0
## 10 BH Low 511. 0.234 1976 10 85.0 0 23
## # ℹ 50 more rows
## # ℹ 2 more variables: tmn <dbl>, tmx <dbl>#some cases where ppt remains high in following month
growyear_months <- tibble(month=c(1:12), growmonth=c(6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5))
nosnow_historical_growyear_months <- full_join(growyear_months, nosnow_pops_historical_years)## Joining with `by = join_by(month)`dim(nosnow_historical_growyear_months)## [1] 1800 12nosnow_first_month <- nosnow_historical_growyear_months %>%
group_by(parent.pop, elevation.group, elev_m, year) %>%
filter(ppt>=25) %>%
arrange(growmonth) %>%
filter(row_number()==1) #get first month for each pop and year with germinating inducing rain
nosnow_first_month_tomerge <- nosnow_first_month %>%
select(parent.pop:elev_m, year, firstmonth=growmonth)
nosnow_first_month_col <- full_join(nosnow_historical_growyear_months, nosnow_first_month_tomerge)## Joining with `by = join_by(parent.pop, elevation.group, elev_m, year)`dim(nosnow_first_month_col)## [1] 1800 13nosnow_last_month <- nosnow_historical_growyear_months %>%
group_by(parent.pop, elevation.group, elev_m, year) %>%
filter(cwd>87) %>% #adjusted for the historical 3rd quartile of cwd
arrange(month) %>%
filter(row_number()==1)
nosnow_last_month_tomerge <- nosnow_last_month %>%
select(parent.pop:elev_m, year, lastmonth=growmonth) #last month is in grow month not calendar month format
nosnow_last_month_col <- full_join(nosnow_first_month_col, nosnow_last_month_tomerge)## Joining with `by = join_by(parent.pop, elevation.group, elev_m, year)`dim(nosnow_last_month_col)## [1] 1800 14#Checking for weird cases
nosnow_last_month_col %>% filter(is.na(lastmonth)) %>% arrange(year) #0 years where there isn't a last month## # A tibble: 0 × 14
## # ℹ 14 variables: month <dbl>, growmonth <dbl>, parent.pop <chr>,
## # elevation.group <chr>, elev_m <dbl>, PckSum <dbl>, year <chr>, cwd <dbl>,
## # pck <dbl>, ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_last_month_col %>% filter(is.na(firstmonth)) %>% arrange(year) #0 years wehre there isn't a first month## # A tibble: 0 × 14
## # ℹ 14 variables: month <dbl>, growmonth <dbl>, parent.pop <chr>,
## # elevation.group <chr>, elev_m <dbl>, PckSum <dbl>, year <chr>, cwd <dbl>,
## # pck <dbl>, ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_last_month_col %>% filter(lastmonth<5) %>% arrange(parent.pop, year) #some cases where last month is less than 5 (earlier than December)## # A tibble: 420 × 14
## month growmonth parent.pop elevation.group elev_m PckSum year cwd pck
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 6 BH Low 511. 0.234 1982 24.3 7.02
## 2 2 7 BH Low 511. 0.234 1982 37.8 0
## 3 3 8 BH Low 511. 0.234 1982 39.6 0
## 4 4 9 BH Low 511. 0.234 1982 38.8 0
## 5 5 10 BH Low 511. 0.234 1982 74.0 0
## 6 6 11 BH Low 511. 0.234 1982 33.9 0
## 7 7 12 BH Low 511. 0.234 1982 79.9 0
## 8 8 1 BH Low 511. 0.234 1982 177. 0
## 9 9 2 BH Low 511. 0.234 1982 129. 0
## 10 10 3 BH Low 511. 0.234 1982 79.7 0
## # ℹ 410 more rows
## # ℹ 5 more variables: ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_last_month_col %>% filter(lastmonth<firstmonth, lastmonth<5) %>% arrange(parent.pop, year) #most of the above are when the last month is before the first month (in growyear, not calendar year)## # A tibble: 384 × 14
## month growmonth parent.pop elevation.group elev_m PckSum year cwd pck
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 6 BH Low 511. 0.234 1982 24.3 7.02
## 2 2 7 BH Low 511. 0.234 1982 37.8 0
## 3 3 8 BH Low 511. 0.234 1982 39.6 0
## 4 4 9 BH Low 511. 0.234 1982 38.8 0
## 5 5 10 BH Low 511. 0.234 1982 74.0 0
## 6 6 11 BH Low 511. 0.234 1982 33.9 0
## 7 7 12 BH Low 511. 0.234 1982 79.9 0
## 8 8 1 BH Low 511. 0.234 1982 177. 0
## 9 9 2 BH Low 511. 0.234 1982 129. 0
## 10 10 3 BH Low 511. 0.234 1982 79.7 0
## # ℹ 374 more rows
## # ℹ 5 more variables: ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_last_month_col %>% filter(lastmonth>firstmonth & lastmonth<5) %>% arrange(parent.pop, year) #0 cases wehre the last month is after the first month before Dec ## # A tibble: 0 × 14
## # ℹ 14 variables: month <dbl>, growmonth <dbl>, parent.pop <chr>,
## # elevation.group <chr>, elev_m <dbl>, PckSum <dbl>, year <chr>, cwd <dbl>,
## # pck <dbl>, ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_last_month_col %>% filter(lastmonth==5) %>% arrange(parent.pop, year) #0 cases where 5 is the last month ## # A tibble: 0 × 14
## # ℹ 14 variables: month <dbl>, growmonth <dbl>, parent.pop <chr>,
## # elevation.group <chr>, elev_m <dbl>, PckSum <dbl>, year <chr>, cwd <dbl>,
## # pck <dbl>, ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_last_month_col %>% filter(lastmonth==firstmonth) %>% arrange(parent.pop, year) #first month and last month are the same for IH in 3 years ('72, '85, '89)## # A tibble: 36 × 14
## month growmonth parent.pop elevation.group elev_m PckSum year cwd pck
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 6 IH Low 454. 17.3 1972 18.3 19.6
## 2 2 7 IH Low 454. 17.3 1972 28.4 0
## 3 3 8 IH Low 454. 17.3 1972 43.9 0
## 4 4 9 IH Low 454. 17.3 1972 48.9 0
## 5 5 10 IH Low 454. 17.3 1972 51.8 0
## 6 6 11 IH Low 454. 17.3 1972 56.7 0
## 7 7 12 IH Low 454. 17.3 1972 70.3 0
## 8 8 1 IH Low 454. 17.3 1972 86.0 0
## 9 9 2 IH Low 454. 17.3 1972 88.3 0
## 10 10 3 IH Low 454. 17.3 1972 58.9 0
## # ℹ 26 more rows
## # ℹ 5 more variables: ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_last_month_col %>% filter(growmonth==firstmonth+1, cwd>88) %>% arrange(parent.pop, year) #8 cases where cwd is high in the second growth month ## # A tibble: 8 × 14
## month growmonth parent.pop elevation.group elev_m PckSum year cwd pck
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 9 2 BH Low 511. 0.234 1976 115. 0
## 2 9 2 CC Low 313 0.951 1965 97.5 0
## 3 9 2 CC Low 313 0.951 1968 104. 0
## 4 9 2 CC Low 313 0.951 1976 93.7 0
## 5 9 2 IH Low 454. 17.3 1975 130. 0
## 6 9 2 SC Low 422. 0.597 1975 123. 0
## 7 9 2 SC Low 422. 0.597 1976 105 0
## 8 9 2 TM2 Low 379. 5.47 1968 89.6 0
## # ℹ 5 more variables: ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>nosnow_grwseason_historical <- nosnow_last_month_col %>% #fill in all the months b/t the first and last for the full growth season
group_by(parent.pop, elevation.group, elev_m, year) %>%
filter(growmonth>=firstmonth) %>% #first and last month are in grow month format not calendar year
filter(ifelse(lastmonth<5, growmonth<=12, growmonth<lastmonth))
summary(nosnow_grwseason_historical) ## month growmonth parent.pop elevation.group
## Min. : 1.000 Min. : 1.000 Length:1310 Length:1310
## 1st Qu.: 3.000 1st Qu.: 5.000 Class :character Class :character
## Median : 5.000 Median : 7.000 Mode :character Mode :character
## Mean : 6.048 Mean : 6.724
## 3rd Qu.:10.000 3rd Qu.: 9.000
## Max. :12.000 Max. :12.000
## elev_m PckSum year cwd
## Min. :313.0 Min. : 0.2340 Length:1310 Min. : 0.00
## 1st Qu.:379.2 1st Qu.: 0.5973 Class :character 1st Qu.: 21.76
## Median :421.5 Median : 0.9510 Mode :character Median : 34.30
## Mean :415.4 Mean : 5.4384 Mean : 41.94
## 3rd Qu.:454.1 3rd Qu.: 5.4673 3rd Qu.: 62.44
## Max. :511.4 Max. :17.3440 Max. :168.60
## pck ppt tmn tmx
## Min. : 0.0000 Min. : 0.00 Min. :-1.850 Min. : 8.63
## 1st Qu.: 0.0000 1st Qu.: 26.93 1st Qu.: 2.800 1st Qu.:13.98
## Median : 0.0000 Median : 66.74 Median : 5.125 Median :17.50
## Mean : 0.5632 Mean :101.04 Mean : 5.889 Mean :19.03
## 3rd Qu.: 0.0000 3rd Qu.:146.22 3rd Qu.: 8.695 3rd Qu.:23.99
## Max. :129.4600 Max. :559.96 Max. :17.920 Max. :35.85
## firstmonth lastmonth
## Min. :1.000 Min. : 1.000
## 1st Qu.:2.000 1st Qu.: 2.000
## Median :3.000 Median :11.000
## Mean :2.768 Mean : 8.463
## 3rd Qu.:3.000 3rd Qu.:11.000
## Max. :5.000 Max. :12.000nosnow_grwseason_historical %>% filter(cwd > 88) %>% filter(growmonth==firstmonth) #some cases when cwd is high in the first month (when ppt >25)## # A tibble: 33 × 14
## # Groups: parent.pop, elevation.group, elev_m, year [33]
## month growmonth parent.pop elevation.group elev_m PckSum year cwd pck
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 8 1 BH Low 511. 0.234 1976 168. 0
## 2 8 1 IH Low 454. 17.3 1975 94.4 0
## 3 8 1 IH Low 454. 17.3 1976 152. 0
## 4 8 1 SC Low 422. 0.597 1975 169. 0
## 5 8 1 SC Low 422. 0.597 1976 148. 0
## 6 8 1 TM2 Low 379. 5.47 1965 156. 0
## 7 8 1 TM2 Low 379. 5.47 1968 151. 0
## 8 8 1 TM2 Low 379. 5.47 1976 150. 0
## 9 9 2 BH Low 511. 0.234 1978 107. 0
## 10 9 2 BH Low 511. 0.234 1982 129. 0
## # ℹ 23 more rows
## # ℹ 5 more variables: ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>xtabs(~parent.pop+month, data=nosnow_grwseason_historical)## month
## parent.pop 1 2 3 4 5 6 7 8 9 10 11 12
## BH 30 30 30 30 30 5 1 1 5 19 28 30
## CC 30 30 30 30 30 13 2 3 12 26 29 30
## IH 30 30 30 30 30 30 28 2 11 24 30 30
## SC 30 30 30 30 30 9 0 2 7 22 29 30
## TM2 30 30 30 30 17 9 4 3 12 27 30 30options(max.print=1000000)
xtabs(~year+month+parent.pop, data=nosnow_grwseason_historical)## , , parent.pop = BH
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 1 1 1 1 1 0 0 0 0 1 1 1
## 1963 1 1 1 1 1 0 0 0 0 1 1 1
## 1964 1 1 1 1 1 1 0 0 0 1 1 1
## 1965 1 1 1 1 1 0 0 0 0 0 1 1
## 1966 1 1 1 1 1 0 0 0 0 0 1 1
## 1967 1 1 1 1 1 1 0 0 0 0 1 1
## 1968 1 1 1 1 1 0 0 0 0 1 1 1
## 1969 1 1 1 1 1 0 0 0 0 1 1 1
## 1970 1 1 1 1 1 1 0 0 0 0 1 1
## 1971 1 1 1 1 1 0 0 0 0 0 1 1
## 1972 1 1 1 1 1 0 0 0 0 0 1 1
## 1973 1 1 1 1 1 0 0 0 0 1 1 1
## 1974 1 1 1 1 1 0 0 0 0 1 1 1
## 1975 1 1 1 1 1 0 0 0 0 1 1 1
## 1976 1 1 1 1 1 0 0 1 1 1 1 1
## 1977 1 1 1 1 1 0 0 0 0 0 1 1
## 1978 1 1 1 1 1 0 0 0 1 1 1 1
## 1979 1 1 1 1 1 0 0 0 0 1 1 1
## 1980 1 1 1 1 1 0 0 0 0 0 0 1
## 1981 1 1 1 1 1 0 0 0 0 1 1 1
## 1982 1 1 1 1 1 1 1 0 1 1 1 1
## 1983 1 1 1 1 1 0 0 0 0 0 1 1
## 1984 1 1 1 1 1 1 0 0 0 1 1 1
## 1985 1 1 1 1 1 0 0 0 0 1 1 1
## 1986 1 1 1 1 1 0 0 0 1 1 1 1
## 1987 1 1 1 1 1 0 0 0 0 1 1 1
## 1988 1 1 1 1 1 0 0 0 0 0 1 1
## 1989 1 1 1 1 1 0 0 0 1 1 1 1
## 1990 1 1 1 1 1 0 0 0 0 0 0 1
## 1991 1 1 1 1 1 0 0 0 0 1 1 1
##
## , , parent.pop = CC
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 1 1 1 1 1 0 0 0 0 1 1 1
## 1963 1 1 1 1 1 1 0 0 0 1 1 1
## 1964 1 1 1 1 1 1 0 0 0 1 1 1
## 1965 1 1 1 1 1 1 0 1 1 1 1 1
## 1966 1 1 1 1 1 0 0 0 0 0 1 1
## 1967 1 1 1 1 1 1 0 0 0 1 1 1
## 1968 1 1 1 1 1 0 0 1 1 1 1 1
## 1969 1 1 1 1 1 1 0 0 0 1 1 1
## 1970 1 1 1 1 1 1 0 0 0 1 1 1
## 1971 1 1 1 1 1 1 0 0 0 0 1 1
## 1972 1 1 1 1 1 0 0 0 1 1 1 1
## 1973 1 1 1 1 1 0 0 0 0 1 1 1
## 1974 1 1 1 1 1 0 0 0 0 1 1 1
## 1975 1 1 1 1 1 0 0 0 0 1 1 1
## 1976 1 1 1 1 1 0 0 1 1 1 1 1
## 1977 1 1 1 1 1 0 0 0 1 1 1 1
## 1978 1 1 1 1 1 0 0 0 1 1 1 1
## 1979 1 1 1 1 1 0 0 0 0 1 1 1
## 1980 1 1 1 1 1 1 1 0 0 0 0 1
## 1981 1 1 1 1 1 0 0 0 1 1 1 1
## 1982 1 1 1 1 1 1 1 0 1 1 1 1
## 1983 1 1 1 1 1 0 0 0 1 1 1 1
## 1984 1 1 1 1 1 1 0 0 0 1 1 1
## 1985 1 1 1 1 1 0 0 0 1 1 1 1
## 1986 1 1 1 1 1 0 0 0 1 1 1 1
## 1987 1 1 1 1 1 0 0 0 0 1 1 1
## 1988 1 1 1 1 1 1 0 0 0 0 1 1
## 1989 1 1 1 1 1 0 0 0 1 1 1 1
## 1990 1 1 1 1 1 1 0 0 0 1 1 1
## 1991 1 1 1 1 1 1 0 0 0 1 1 1
##
## , , parent.pop = IH
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 1 1 1 1 1 1 1 0 0 1 1 1
## 1963 1 1 1 1 1 1 1 0 0 1 1 1
## 1964 1 1 1 1 1 1 1 0 0 1 1 1
## 1965 1 1 1 1 1 1 1 0 0 0 1 1
## 1966 1 1 1 1 1 1 1 0 0 0 1 1
## 1967 1 1 1 1 1 1 1 0 0 1 1 1
## 1968 1 1 1 1 1 1 1 0 0 1 1 1
## 1969 1 1 1 1 1 1 1 0 0 1 1 1
## 1970 1 1 1 1 1 1 1 0 0 1 1 1
## 1971 1 1 1 1 1 1 1 0 0 1 1 1
## 1972 1 1 1 1 1 1 1 0 1 1 1 1
## 1973 1 1 1 1 1 1 1 0 1 1 1 1
## 1974 1 1 1 1 1 1 1 0 0 1 1 1
## 1975 1 1 1 1 1 1 1 1 1 1 1 1
## 1976 1 1 1 1 1 1 0 1 1 1 1 1
## 1977 1 1 1 1 1 1 0 0 0 0 1 1
## 1978 1 1 1 1 1 1 1 0 1 1 1 1
## 1979 1 1 1 1 1 1 1 0 0 1 1 1
## 1980 1 1 1 1 1 1 1 0 0 0 1 1
## 1981 1 1 1 1 1 1 1 0 1 1 1 1
## 1982 1 1 1 1 1 1 1 0 1 1 1 1
## 1983 1 1 1 1 1 1 1 0 1 1 1 1
## 1984 1 1 1 1 1 1 1 0 0 1 1 1
## 1985 1 1 1 1 1 1 1 0 1 1 1 1
## 1986 1 1 1 1 1 1 1 0 1 1 1 1
## 1987 1 1 1 1 1 1 1 0 0 1 1 1
## 1988 1 1 1 1 1 1 1 0 0 0 1 1
## 1989 1 1 1 1 1 1 1 0 1 1 1 1
## 1990 1 1 1 1 1 1 1 0 0 0 1 1
## 1991 1 1 1 1 1 1 1 0 0 1 1 1
##
## , , parent.pop = SC
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 1 1 1 1 1 0 0 0 0 1 1 1
## 1963 1 1 1 1 1 1 0 0 0 1 1 1
## 1964 1 1 1 1 1 1 0 0 0 1 1 1
## 1965 1 1 1 1 1 0 0 0 0 0 1 1
## 1966 1 1 1 1 1 0 0 0 0 0 1 1
## 1967 1 1 1 1 1 1 0 0 0 1 1 1
## 1968 1 1 1 1 1 0 0 0 0 1 1 1
## 1969 1 1 1 1 1 0 0 0 0 1 1 1
## 1970 1 1 1 1 1 1 0 0 0 1 1 1
## 1971 1 1 1 1 1 1 0 0 0 0 1 1
## 1972 1 1 1 1 1 0 0 0 1 1 1 1
## 1973 1 1 1 1 1 0 0 0 0 1 1 1
## 1974 1 1 1 1 1 1 0 0 0 1 1 1
## 1975 1 1 1 1 1 0 0 1 1 1 1 1
## 1976 1 1 1 1 1 0 0 1 1 1 1 1
## 1977 1 1 1 1 1 0 0 0 0 0 1 1
## 1978 1 1 1 1 1 0 0 0 1 1 1 1
## 1979 1 1 1 1 1 0 0 0 0 1 1 1
## 1980 1 1 1 1 1 1 0 0 0 0 0 1
## 1981 1 1 1 1 1 0 0 0 0 1 1 1
## 1982 1 1 1 1 1 0 0 0 1 1 1 1
## 1983 1 1 1 1 1 0 0 0 0 1 1 1
## 1984 1 1 1 1 1 1 0 0 0 1 1 1
## 1985 1 1 1 1 1 0 0 0 0 1 1 1
## 1986 1 1 1 1 1 0 0 0 1 1 1 1
## 1987 1 1 1 1 1 0 0 0 0 0 1 1
## 1988 1 1 1 1 1 0 0 0 0 0 1 1
## 1989 1 1 1 1 1 0 0 0 1 1 1 1
## 1990 1 1 1 1 1 0 0 0 0 0 1 1
## 1991 1 1 1 1 1 1 0 0 0 1 1 1
##
## , , parent.pop = TM2
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 1 1 1 1 1 0 0 0 0 1 1 1
## 1963 1 1 1 1 1 1 0 0 0 1 1 1
## 1964 1 1 1 1 0 0 0 0 0 1 1 1
## 1965 1 1 1 1 1 0 0 1 1 1 1 1
## 1966 1 1 1 1 0 0 0 0 0 0 1 1
## 1967 1 1 1 1 1 1 1 0 0 1 1 1
## 1968 1 1 1 1 0 0 0 1 1 1 1 1
## 1969 1 1 1 1 1 1 0 0 0 1 1 1
## 1970 1 1 1 1 0 0 0 0 0 1 1 1
## 1971 1 1 1 1 1 0 0 0 0 0 1 1
## 1972 1 1 1 1 0 0 0 0 1 1 1 1
## 1973 1 1 1 1 0 0 0 0 0 1 1 1
## 1974 1 1 1 1 1 0 0 0 0 1 1 1
## 1975 1 1 1 1 1 0 0 0 0 1 1 1
## 1976 1 1 1 1 0 0 0 1 1 1 1 1
## 1977 1 1 1 1 1 0 0 0 1 1 1 1
## 1978 1 1 1 1 1 0 0 0 1 1 1 1
## 1979 1 1 1 1 0 0 0 0 0 1 1 1
## 1980 1 1 1 1 1 1 1 0 0 1 1 1
## 1981 1 1 1 1 0 0 0 0 1 1 1 1
## 1982 1 1 1 1 1 1 1 0 1 1 1 1
## 1983 1 1 1 1 1 1 1 0 1 1 1 1
## 1984 1 1 1 1 0 0 0 0 0 1 1 1
## 1985 1 1 1 1 0 0 0 0 1 1 1 1
## 1986 1 1 1 1 1 0 0 0 1 1 1 1
## 1987 1 1 1 1 0 0 0 0 0 1 1 1
## 1988 1 1 1 1 1 1 0 0 0 0 1 1
## 1989 1 1 1 1 0 0 0 0 1 1 1 1
## 1990 1 1 1 1 1 1 0 0 0 1 1 1
## 1991 1 1 1 1 1 1 0 0 0 1 1 1#IH grows all year in 1975
nosnow_grwseason_historical %>% ggplot(aes(x=month)) + geom_histogram() +
scale_x_continuous(breaks=c(1, 2, 3, 4, 5,6,7,8,9,10, 11, 12)) +
facet_wrap(~parent.pop)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
- Populations that get more than 70 mm of snow pack in a year (on average): growth season = when snowpack < 70 mm With <70 mm as the only criteria, a lot of pops grow all year and months aren’t consecutive. Need to try first month as pck==0 and following months as pck <70mm
Recent climate
snow_pops_recent <- pop_elev_climate_recent_avgs %>% filter(PckSum >= 70)
unique(snow_pops_recent$parent.pop) #18 pops get some significant snowpack per year ## [1] "CP2" "CP3" "DPR" "FR" "LV1" "LV3" "LVTR1" "SQ1" "SQ2"
## [10] "SQ3" "WL1" "WL2" "WR" "WV" "YO11" "YO4" "YO7" "YO8"summary(snow_pops_recent)## parent.pop elevation.group elev_m month
## Length:216 Length:216 Min. : 748.9 Min. : 1.00
## Class :character Class :character 1st Qu.:1613.8 1st Qu.: 3.75
## Mode :character Mode :character Median :2200.9 Median : 6.50
## Mean :1992.5 Mean : 6.50
## 3rd Qu.:2470.0 3rd Qu.: 9.25
## Max. :2872.3 Max. :12.00
## cwd_mean pck_mean ppt_mean tmn_mean
## Min. : 9.286 Min. : 0.00 Min. : 0.1547 Min. :-9.566
## 1st Qu.: 27.299 1st Qu.: 0.00 1st Qu.: 16.7425 1st Qu.:-3.289
## Median : 44.967 Median : 27.01 Median : 85.8398 Median : 1.360
## Mean : 54.108 Mean : 167.47 Mean :105.1475 Mean : 1.965
## 3rd Qu.: 80.545 3rd Qu.: 237.79 3rd Qu.:175.5006 3rd Qu.: 6.873
## Max. :147.992 Max. :1112.78 Max. :355.7843 Max. :16.219
## tmx_mean cwd_sem pck_sem ppt_sem
## Min. : 1.758 Min. :0.1430 Min. : 0.00 Min. : 0.07985
## 1st Qu.: 8.172 1st Qu.:0.5688 1st Qu.: 0.00 1st Qu.: 3.65977
## Median :13.369 Median :1.2517 Median : 11.36 Median :13.34647
## Mean :14.729 Mean :1.7305 Mean : 23.43 Mean :14.96105
## 3rd Qu.:21.431 3rd Qu.:2.6690 3rd Qu.: 43.69 3rd Qu.:23.82075
## Max. :32.650 Max. :8.9076 Max. :106.86 Max. :47.97252
## tmn_sem tmx_sem PckSum
## Min. :0.1900 Min. :0.1804 Min. : 107.5
## 1st Qu.:0.2602 1st Qu.:0.3041 1st Qu.: 613.4
## Median :0.2912 Median :0.3836 Median :1587.7
## Mean :0.2906 Mean :0.3643 Mean :2009.6
## 3rd Qu.:0.3181 3rd Qu.:0.4157 3rd Qu.:2655.8
## Max. :0.4145 Max. :0.5143 Max. :5408.1snow_pops_recent_tojoin <- snow_pops_recent %>% select(parent.pop:elev_m, PckSum) %>% distinct()
snow_pops_recent_years <- left_join(snow_pops_recent_tojoin, pop_elev_climate_recent) ## Joining with `by = join_by(parent.pop, elevation.group, elev_m)`summary(snow_pops_recent_years)## parent.pop elevation.group elev_m PckSum
## Length:6480 Length:6480 Min. : 748.9 Min. : 107.5
## Class :character Class :character 1st Qu.:1613.8 1st Qu.: 613.4
## Mode :character Mode :character Median :2200.9 Median :1587.7
## Mean :1992.5 Mean :2009.6
## 3rd Qu.:2470.0 3rd Qu.:2655.8
## Max. :2872.3 Max. :5408.1
## year month cwd pck
## Length:6480 Min. : 1.00 Min. : 0.00 Min. : 0.0
## Class :character 1st Qu.: 3.75 1st Qu.: 25.14 1st Qu.: 0.0
## Mode :character Median : 6.50 Median : 45.47 Median : 0.0
## Mean : 6.50 Mean : 54.11 Mean : 167.5
## 3rd Qu.: 9.25 3rd Qu.: 81.18 3rd Qu.: 197.0
## Max. :12.00 Max. :182.70 Max. :2183.6
## ppt tmn tmx
## Min. : 0.000 Min. :-13.180 Min. :-3.22
## 1st Qu.: 9.678 1st Qu.: -3.190 1st Qu.: 8.05
## Median : 52.040 Median : 1.480 Median :13.61
## Mean :105.147 Mean : 1.965 Mean :14.73
## 3rd Qu.:153.838 3rd Qu.: 6.902 3rd Qu.:21.44
## Max. :981.420 Max. : 19.730 Max. :35.13snow_first_month <- snow_pops_recent_years %>%
group_by(parent.pop, elevation.group, elev_m, year) %>%
filter(pck==0) %>%
arrange(month) %>%
filter(row_number()==1) #get first month for each pop and year with no snowpack for germ
snow_first_month_tomerge <- snow_first_month %>%
select(parent.pop:elev_m, year, firstmonth=month)
snow_first_month_col <- full_join(snow_pops_recent_years, snow_first_month_tomerge)## Joining with `by = join_by(parent.pop, elevation.group, elev_m, year)`dim(snow_first_month_col)## [1] 6480 12snow_last_month <- snow_first_month_col %>%
group_by(parent.pop, elevation.group, elev_m, year) %>%
filter(month>firstmonth) %>%
filter(pck>70) %>%
arrange(month) %>%
filter(row_number()==1) #get first month after growstart for each pop and year with pck >70
snow_last_month_tomerge <- snow_last_month %>%
select(parent.pop:elev_m, year, firstmonth,lastmonth=month)
snow_last_month_col <- full_join(snow_first_month_col, snow_last_month_tomerge)## Joining with `by = join_by(parent.pop, elevation.group, elev_m, year,
## firstmonth)`dim(snow_last_month_col)## [1] 6480 13#check weird cases
snow_last_month_col %>% filter(is.na(firstmonth)) #no cases where there isn't a firstmonth## # A tibble: 0 × 13
## # Groups: parent.pop, elevation.group, elev_m [0]
## # ℹ 13 variables: parent.pop <chr>, elevation.group <chr>, elev_m <dbl>,
## # PckSum <dbl>, year <chr>, month <dbl>, cwd <dbl>, pck <dbl>, ppt <dbl>,
## # tmn <dbl>, tmx <dbl>, firstmonth <dbl>, lastmonth <dbl>snow_last_month_col %>% filter(is.na(lastmonth)) #198 cases where there isn't a lastmonth ## # A tibble: 2,376 × 13
## # Groups: parent.pop, elevation.group, elev_m [18]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 CP2 High 2244. 2440. 1999 1 21.9 421. 299.
## 2 CP2 High 2244. 2440. 1999 2 22.4 757. 388.
## 3 CP2 High 2244. 2440. 1999 3 33.3 779. 110.
## 4 CP2 High 2244. 2440. 1999 4 32.9 769. 139.
## 5 CP2 High 2244. 2440. 1999 5 84.1 343. 26.1
## 6 CP2 High 2244. 2440. 1999 6 111. 0 20.4
## 7 CP2 High 2244. 2440. 1999 7 127. 0 7.67
## 8 CP2 High 2244. 2440. 1999 8 89.8 0 28.3
## 9 CP2 High 2244. 2440. 1999 9 101. 0 7.94
## 10 CP2 High 2244. 2440. 1999 10 70.0 0 53.8
## # ℹ 2,366 more rows
## # ℹ 4 more variables: tmn <dbl>, tmx <dbl>, firstmonth <dbl>, lastmonth <dbl>snow_last_month_col %>% filter(lastmonth==firstmonth+1) #12 cases where there was sig snowpack in the month after the first month of 0 snowpack ## # A tibble: 144 × 13
## # Groups: parent.pop, elevation.group, elev_m [9]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt tmn
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CP2 High 2244. 2440. 2014 1 26.1 0 72.1 -1.45
## 2 CP2 High 2244. 2440. 2014 2 30.3 150. 267. -3.69
## 3 CP2 High 2244. 2440. 2014 3 42.7 129. 160. -2.43
## 4 CP2 High 2244. 2440. 2014 4 61.6 0 87.3 -1.54
## 5 CP2 High 2244. 2440. 2014 5 88.3 0 38.3 2.53
## 6 CP2 High 2244. 2440. 2014 6 118. 0 2.77 6.62
## 7 CP2 High 2244. 2440. 2014 7 90.1 0 30.3 12.3
## 8 CP2 High 2244. 2440. 2014 8 94.7 0 14.9 10.0
## 9 CP2 High 2244. 2440. 2014 9 91.7 0 41.0 8.24
## 10 CP2 High 2244. 2440. 2014 10 69.0 0 19.6 3.91
## # ℹ 134 more rows
## # ℹ 3 more variables: tmx <dbl>, firstmonth <dbl>, lastmonth <dbl>snow_last_month_col %>% filter(lastmonth==firstmonth+2) #3 cases where there was sig snowpack in the second month after the first month of 0 snowpack ## # A tibble: 36 × 13
## # Groups: parent.pop, elevation.group, elev_m [3]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 SQ1 Mid 1921. 613. 2018 1 32.8 0 90.7
## 2 SQ1 Mid 1921. 613. 2018 2 37.8 0 24.2
## 3 SQ1 Mid 1921. 613. 2018 3 28.9 84.9 343.
## 4 SQ1 Mid 1921. 613. 2018 4 52.1 0 86.1
## 5 SQ1 Mid 1921. 613. 2018 5 76.0 0 14.4
## 6 SQ1 Mid 1921. 613. 2018 6 95.7 0 0.0600
## 7 SQ1 Mid 1921. 613. 2018 7 150. 0 10.8
## 8 SQ1 Mid 1921. 613. 2018 8 158. 0 0.180
## 9 SQ1 Mid 1921. 613. 2018 9 127. 0 0.870
## 10 SQ1 Mid 1921. 613. 2018 10 86.1 0 58.0
## # ℹ 26 more rows
## # ℹ 4 more variables: tmn <dbl>, tmx <dbl>, firstmonth <dbl>, lastmonth <dbl>snow_grwseason_recent <- snow_last_month_col %>% #fill in months b/t start and stop
group_by(parent.pop, elevation.group, elev_m, year) %>%
filter(month>=firstmonth) %>%
filter(ifelse(is.na(lastmonth), pck<70, month<lastmonth))
summary(snow_grwseason_recent) ## parent.pop elevation.group elev_m PckSum
## Length:4000 Length:4000 Min. : 748.9 Min. : 107.5
## Class :character Class :character 1st Qu.:1158.0 1st Qu.: 391.1
## Mode :character Mode :character Median :2020.1 Median :1461.6
## Mean :1853.7 Mean :1653.4
## 3rd Qu.:2373.2 3rd Qu.:2649.6
## Max. :2872.3 Max. :5408.1
##
## year month cwd pck
## Length:4000 Min. : 1.000 Min. : 0.00 Min. : 0.000
## Class :character 1st Qu.: 6.000 1st Qu.: 39.01 1st Qu.: 0.000
## Mode :character Median : 8.000 Median : 66.81 Median : 0.000
## Mean : 7.707 Mean : 67.51 Mean : 1.413
## 3rd Qu.:10.000 3rd Qu.: 93.33 3rd Qu.: 0.000
## Max. :12.000 Max. :182.70 Max. :69.930
##
## ppt tmn tmx firstmonth
## Min. : 0.000 Min. :-10.440 Min. : 2.09 Min. :1.000
## 1st Qu.: 3.138 1st Qu.: 1.490 1st Qu.:13.55 1st Qu.:3.000
## Median : 20.455 Median : 5.140 Median :19.36 Median :5.000
## Mean : 51.358 Mean : 5.346 Mean :19.01 Mean :4.123
## 3rd Qu.: 66.570 3rd Qu.: 9.240 3rd Qu.:23.72 3rd Qu.:6.000
## Max. :803.250 Max. : 19.730 Max. :35.13 Max. :8.000
##
## lastmonth
## Min. : 2.00
## 1st Qu.:12.00
## Median :12.00
## Mean :11.72
## 3rd Qu.:12.00
## Max. :12.00
## NA's :1780xtabs(~parent.pop+month, data=snow_grwseason_recent)## month
## parent.pop 1 2 3 4 5 6 7 8 9 10 11 12
## CP2 1 0 1 2 16 26 29 29 29 29 24 7
## CP3 0 0 0 1 14 26 30 30 30 30 24 7
## DPR 17 22 27 28 28 28 28 28 28 28 28 24
## FR 11 14 23 30 30 30 30 30 30 30 30 22
## LV1 0 0 0 0 3 17 28 30 30 30 11 1
## LV3 0 0 0 0 4 17 28 30 30 30 14 1
## LVTR1 0 0 0 0 3 17 28 30 30 30 11 1
## SQ1 6 7 8 18 27 28 28 28 28 28 28 16
## SQ2 5 6 7 17 26 28 28 28 28 28 28 16
## SQ3 1 1 2 8 22 28 30 30 30 30 29 12
## WL1 7 6 9 19 27 27 27 27 27 27 26 14
## WL2 1 0 1 8 21 28 29 29 29 29 28 9
## WR 12 11 19 25 28 28 28 28 28 28 28 18
## WV 7 13 21 30 30 30 30 30 30 30 30 18
## YO11 0 0 0 0 13 26 30 30 30 30 23 8
## YO4 1 3 2 9 22 29 29 29 29 29 28 10
## YO7 1 0 1 1 12 25 29 29 29 29 22 7
## YO8 0 0 0 2 12 25 30 30 30 30 23 7options(max.print=1000000)
xtabs(~year+month+parent.pop, data=snow_grwseason_recent)## , , parent.pop = CP2
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1992 0 0 0 1 1 1 1 1 1 1 1 0
## 1993 0 0 0 0 0 1 1 1 1 1 1 0
## 1994 0 0 0 0 1 1 1 1 1 1 0 0
## 1995 0 0 0 0 0 0 1 1 1 1 1 0
## 1996 0 0 0 0 0 1 1 1 1 1 1 0
## 1997 0 0 0 0 0 1 1 1 1 1 1 0
## 1998 0 0 0 0 0 0 1 1 1 1 0 0
## 1999 0 0 0 0 0 1 1 1 1 1 1 1
## 2000 0 0 0 0 1 1 1 1 1 1 1 1
## 2001 0 0 0 0 1 1 1 1 1 1 1 0
## 2002 0 0 0 0 1 1 1 1 1 1 1 0
## 2003 0 0 0 0 0 1 1 1 1 1 0 0
## 2004 0 0 0 0 1 1 1 1 1 1 1 0
## 2005 0 0 0 0 0 1 1 1 1 1 1 0
## 2006 0 0 0 0 0 1 1 1 1 1 1 0
## 2007 0 0 0 0 1 1 1 1 1 1 1 0
## 2008 0 0 0 0 1 1 1 1 1 1 1 0
## 2009 0 0 0 0 1 1 1 1 1 1 1 0
## 2010 0 0 0 0 0 1 1 1 1 1 0 0
## 2011 0 0 0 0 0 1 1 1 1 1 1 1
## 2012 0 0 0 0 1 1 1 1 1 1 1 0
## 2013 0 0 0 0 1 1 1 1 1 1 1 1
## 2014 1 0 0 0 0 0 0 0 0 0 0 0
## 2015 0 0 1 1 1 1 1 1 1 1 0 0
## 2016 0 0 0 0 1 1 1 1 1 1 1 0
## 2017 0 0 0 0 0 0 1 1 1 1 1 1
## 2018 0 0 0 0 1 1 1 1 1 1 1 1
## 2019 0 0 0 0 0 1 1 1 1 1 1 0
## 2020 0 0 0 0 1 1 1 1 1 1 1 1
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## , , parent.pop = CP3
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## , , parent.pop = DPR
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## , , parent.pop = FR
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## , , parent.pop = LV1
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## , , parent.pop = LV3
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## , , parent.pop = LVTR1
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## , , parent.pop = SQ1
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## , , parent.pop = SQ2
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## , , parent.pop = SQ3
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## , , parent.pop = WL1
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## , , parent.pop = WL2
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##
## , , parent.pop = WR
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## month
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## , , parent.pop = WV
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## , , parent.pop = YO11
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## , , parent.pop = YO4
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## , , parent.pop = YO7
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## 2011 0 0 0 0 0 0 1 1 1 1 1 1
## 2012 0 0 0 0 1 1 1 1 1 1 1 0
## 2013 0 0 0 0 1 1 1 1 1 1 1 1
## 2014 1 0 0 0 0 0 0 0 0 0 0 0
## 2015 0 0 1 1 1 1 1 1 1 1 0 0
## 2016 0 0 0 0 0 1 1 1 1 1 1 0
## 2017 0 0 0 0 0 0 1 1 1 1 1 1
## 2018 0 0 0 0 1 1 1 1 1 1 1 1
## 2019 0 0 0 0 0 1 1 1 1 1 1 0
## 2020 0 0 0 0 1 1 1 1 1 1 1 1
## 2021 0 0 0 0 1 1 1 1 1 1 1 0
##
## , , parent.pop = YO8
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1992 0 0 0 0 1 1 1 1 1 1 1 0
## 1993 0 0 0 0 0 0 1 1 1 1 1 0
## 1994 0 0 0 0 1 1 1 1 1 1 0 0
## 1995 0 0 0 0 0 0 1 1 1 1 1 0
## 1996 0 0 0 0 0 1 1 1 1 1 0 0
## 1997 0 0 0 0 0 1 1 1 1 1 1 0
## 1998 0 0 0 0 0 0 1 1 1 1 0 0
## 1999 0 0 0 0 0 1 1 1 1 1 1 1
## 2000 0 0 0 0 0 1 1 1 1 1 1 1
## 2001 0 0 0 0 1 1 1 1 1 1 0 0
## 2002 0 0 0 0 0 1 1 1 1 1 1 0
## 2003 0 0 0 0 0 1 1 1 1 1 0 0
## 2004 0 0 0 0 0 1 1 1 1 1 1 0
## 2005 0 0 0 0 0 1 1 1 1 1 1 0
## 2006 0 0 0 0 0 1 1 1 1 1 1 0
## 2007 0 0 0 0 1 1 1 1 1 1 1 0
## 2008 0 0 0 0 0 1 1 1 1 1 1 0
## 2009 0 0 0 0 1 1 1 1 1 1 1 0
## 2010 0 0 0 0 0 1 1 1 1 1 0 0
## 2011 0 0 0 0 0 0 1 1 1 1 1 1
## 2012 0 0 0 0 1 1 1 1 1 1 1 0
## 2013 0 0 0 0 1 1 1 1 1 1 1 1
## 2014 0 0 0 1 1 1 1 1 1 1 1 0
## 2015 0 0 0 1 1 1 1 1 1 1 0 0
## 2016 0 0 0 0 0 1 1 1 1 1 1 0
## 2017 0 0 0 0 0 0 1 1 1 1 1 1
## 2018 0 0 0 0 1 1 1 1 1 1 1 1
## 2019 0 0 0 0 0 1 1 1 1 1 1 0
## 2020 0 0 0 0 1 1 1 1 1 1 1 1
## 2021 0 0 0 0 1 1 1 1 1 1 1 0snow_grwseason_recent %>% ggplot(aes(x=month)) + geom_histogram() +
scale_x_continuous(breaks=c(1, 2, 3, 4, 5,6,7,8,9,10, 11, 12)) +
facet_wrap(~parent.pop)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Historical climate
snow_pops_historical <- pop_elev_climate_historical_avgs %>% filter(PckSum >= 70)
unique(snow_pops_historical$parent.pop) #18 pops get some significant snowpack per year ## [1] "CP2" "CP3" "DPR" "FR" "LV1" "LV3" "LVTR1" "SQ1" "SQ2"
## [10] "SQ3" "WL1" "WL2" "WR" "WV" "YO11" "YO4" "YO7" "YO8"summary(snow_pops_historical)## parent.pop elevation.group elev_m month
## Length:216 Length:216 Min. : 748.9 Min. : 1.00
## Class :character Class :character 1st Qu.:1613.8 1st Qu.: 3.75
## Mode :character Mode :character Median :2200.9 Median : 6.50
## Mean :1992.5 Mean : 6.50
## 3rd Qu.:2470.0 3rd Qu.: 9.25
## Max. :2872.3 Max. :12.00
## cwd_mean pck_mean ppt_mean tmn_mean
## Min. : 8.883 Min. : 0.00 Min. : 3.345 Min. :-11.4587
## 1st Qu.: 25.637 1st Qu.: 0.00 1st Qu.: 29.058 1st Qu.: -4.6368
## Median : 43.329 Median : 58.06 Median : 95.651 Median : 0.0515
## Mean : 51.405 Mean : 205.17 Mean :107.936 Mean : 0.3620
## 3rd Qu.: 75.666 3rd Qu.: 320.55 3rd Qu.:181.983 3rd Qu.: 4.7810
## Max. :139.478 Max. :1210.37 Max. :299.498 Max. : 14.3370
## tmx_mean cwd_sem pck_sem ppt_sem
## Min. : 1.071 Min. :0.1581 Min. : 0.00 Min. : 1.194
## 1st Qu.: 7.264 1st Qu.:0.5935 1st Qu.: 0.00 1st Qu.: 5.664
## Median :12.359 Median :1.4092 Median : 17.62 Median :14.549
## Mean :13.822 Mean :1.7050 Mean : 27.37 Mean :15.804
## 3rd Qu.:20.631 3rd Qu.:2.5888 3rd Qu.: 46.21 3rd Qu.:26.063
## Max. :32.312 Max. :7.3270 Max. :112.26 Max. :43.279
## tmn_sem tmx_sem PckSum
## Min. :0.1642 Min. :0.1957 Min. : 228.4
## 1st Qu.:0.2477 1st Qu.:0.3291 1st Qu.: 891.5
## Median :0.2929 Median :0.3840 Median :2072.4
## Mean :0.2943 Mean :0.3860 Mean :2462.0
## 3rd Qu.:0.3254 3rd Qu.:0.4330 3rd Qu.:3377.5
## Max. :0.4556 Max. :0.6157 Max. :6363.1snow_pops_historical_tojoin <- snow_pops_historical %>% select(parent.pop:elev_m, PckSum) %>% distinct()
snow_pops_historical_years <- left_join(snow_pops_historical_tojoin, pop_elev_climate_historical) ## Joining with `by = join_by(parent.pop, elevation.group, elev_m)`summary(snow_pops_historical_years)## parent.pop elevation.group elev_m PckSum
## Length:6480 Length:6480 Min. : 748.9 Min. : 228.4
## Class :character Class :character 1st Qu.:1613.8 1st Qu.: 891.5
## Mode :character Mode :character Median :2200.9 Median :2072.4
## Mean :1992.5 Mean :2462.0
## 3rd Qu.:2470.0 3rd Qu.:3377.5
## Max. :2872.3 Max. :6363.1
## year month cwd pck
## Length:6480 Min. : 1.00 Min. : 0.00 Min. : 0.0
## Class :character 1st Qu.: 3.75 1st Qu.: 24.02 1st Qu.: 0.0
## Mode :character Median : 6.50 Median : 44.88 Median : 0.0
## Mean : 6.50 Mean : 51.40 Mean : 205.2
## 3rd Qu.: 9.25 3rd Qu.: 75.35 3rd Qu.: 275.0
## Max. :12.00 Max. :159.40 Max. :2594.7
## ppt tmn tmx
## Min. : 0.00 Min. :-14.970 Min. :-2.650
## 1st Qu.: 14.03 1st Qu.: -4.480 1st Qu.: 7.107
## Median : 57.01 Median : 0.070 Median :12.885
## Mean :107.94 Mean : 0.362 Mean :13.822
## 3rd Qu.:149.83 3rd Qu.: 5.152 3rd Qu.:20.343
## Max. :951.79 Max. : 16.840 Max. :35.120snow_pops_historical_years %>% filter(pck < 2, pck >0) %>% arrange(parent.pop, pck) #What about when snowpack is 1 mm? This mostly occurs Nov-Jan, one case in Feb and one case in July## # A tibble: 13 × 11
## # Groups: parent.pop, elevation.group, elev_m [9]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 CP2 High 2244. 3158. 1976 1 22.8 0.0300 25.1
## 2 FR Mid 787 261. 1977 1 14.1 0.0800 67
## 3 FR Mid 787 261. 1981 1 13.8 0.480 215.
## 4 FR Mid 787 261. 1964 2 22.7 1.60 11.1
## 5 LV1 High 2593. 6124. 1978 7 76.7 1.61 14.2
## 6 LV1 High 2593. 6124. 1976 12 13.3 1.66 9.28
## 7 LV3 High 2354. 5923. 1976 12 17.1 1.42 9.34
## 8 SQ3 High 2373. 2052. 1991 2 47.0 1.78 31.2
## 9 WL1 Mid 1614. 892. 1979 12 20.8 1.84 162.
## 10 WL2 High 2020. 2093. 1979 11 25.7 0.890 129.
## 11 WL2 High 2020. 2093. 1978 11 24.8 1.36 96.8
## 12 YO11 High 2872. 2452. 1980 11 26.0 0.0500 23.8
## 13 YO8 High 2591. 3391. 1975 11 33.6 1.56 50.4
## # ℹ 2 more variables: tmn <dbl>, tmx <dbl>snow_first_month <- snow_pops_historical_years %>%
group_by(parent.pop, elevation.group, elev_m, year) %>%
filter(pck==0) %>%
arrange(month) %>%
filter(row_number()==1) #get first month for each pop and year with no snowpack for germ
snow_first_month_tomerge <- snow_first_month %>%
select(parent.pop:elev_m, year, firstmonth=month)
snow_first_month_col <- full_join(snow_pops_historical_years, snow_first_month_tomerge)## Joining with `by = join_by(parent.pop, elevation.group, elev_m, year)`dim(snow_first_month_col)## [1] 6480 12snow_last_month <- snow_first_month_col %>%
group_by(parent.pop, elevation.group, elev_m, year) %>%
filter(month>firstmonth) %>%
filter(pck>70) %>%
arrange(month) %>%
filter(row_number()==1) #get first month after growstart for each pop and year with pck >70
snow_last_month_tomerge <- snow_last_month %>%
select(parent.pop:elev_m, year, firstmonth,lastmonth=month)
snow_last_month_col <- full_join(snow_first_month_col, snow_last_month_tomerge)## Joining with `by = join_by(parent.pop, elevation.group, elev_m, year,
## firstmonth)`dim(snow_last_month_col)## [1] 6480 13#check weird cases
snow_last_month_col %>% filter(is.na(firstmonth)) #no cases where there isn't a firstmonth## # A tibble: 0 × 13
## # Groups: parent.pop, elevation.group, elev_m [0]
## # ℹ 13 variables: parent.pop <chr>, elevation.group <chr>, elev_m <dbl>,
## # PckSum <dbl>, year <chr>, month <dbl>, cwd <dbl>, pck <dbl>, ppt <dbl>,
## # tmn <dbl>, tmx <dbl>, firstmonth <dbl>, lastmonth <dbl>snow_last_month_col %>% filter(is.na(lastmonth)) #191 cases where there isn't a lastmonth ## # A tibble: 2,292 × 13
## # Groups: parent.pop, elevation.group, elev_m [18]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt tmn
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CP2 High 2244. 3158. 1962 1 21.3 179. 107. -7.95
## 2 CP2 High 2244. 3158. 1962 2 21.5 650. 479. -8.15
## 3 CP2 High 2244. 3158. 1962 3 16.6 844. 212. -9.09
## 4 CP2 High 2244. 3158. 1962 4 60.1 614. 48.3 -2.58
## 5 CP2 High 2244. 3158. 1962 5 77.5 318. 46.2 -1.77
## 6 CP2 High 2244. 3158. 1962 6 112. 0 6.90 3.23
## 7 CP2 High 2244. 3158. 1962 7 121. 0 11.5 7.48
## 8 CP2 High 2244. 3158. 1962 8 116. 0 7.21 6.18
## 9 CP2 High 2244. 3158. 1962 9 98.9 0 6.55 5.36
## 10 CP2 High 2244. 3158. 1962 10 60.2 0 288. 1.61
## # ℹ 2,282 more rows
## # ℹ 3 more variables: tmx <dbl>, firstmonth <dbl>, lastmonth <dbl>snow_last_month_col %>% filter(lastmonth==firstmonth+1) #9 cases where there was sig snowpack in the month after the first month of 0 snowpack## # A tibble: 108 × 13
## # Groups: parent.pop, elevation.group, elev_m [7]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 DPR Mid 1019. 228. 1962 1 10.9 0 132.
## 2 DPR Mid 1019. 228. 1962 2 11.6 173. 543.
## 3 DPR Mid 1019. 228. 1962 3 10.6 113. 219.
## 4 DPR Mid 1019. 228. 1962 4 43.8 0 55.6
## 5 DPR Mid 1019. 228. 1962 5 38.9 0 21.9
## 6 DPR Mid 1019. 228. 1962 6 40.6 0 0.860
## 7 DPR Mid 1019. 228. 1962 7 0 0 1.09
## 8 DPR Mid 1019. 228. 1962 8 0 0 8.75
## 9 DPR Mid 1019. 228. 1962 9 57.5 0 4.95
## 10 DPR Mid 1019. 228. 1962 10 38.7 0 513.
## # ℹ 98 more rows
## # ℹ 4 more variables: tmn <dbl>, tmx <dbl>, firstmonth <dbl>, lastmonth <dbl>snow_last_month_col %>% filter(lastmonth==firstmonth+2) #8 cases where there was sig snowpack in the second month after the first month of 0 snowpack## # A tibble: 96 × 13
## # Groups: parent.pop, elevation.group, elev_m [8]
## parent.pop elevation.group elev_m PckSum year month cwd pck ppt
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 DPR Mid 1019. 228. 1991 1 11.6 0 27.5
## 2 DPR Mid 1019. 228. 1991 2 21.9 0 91.0
## 3 DPR Mid 1019. 228. 1991 3 10.6 113. 577.
## 4 DPR Mid 1019. 228. 1991 4 40.9 0 66.0
## 5 DPR Mid 1019. 228. 1991 5 5.91 0 96.2
## 6 DPR Mid 1019. 228. 1991 6 0 0 53.8
## 7 DPR Mid 1019. 228. 1991 7 38.6 0 0
## 8 DPR Mid 1019. 228. 1991 8 0 0 9.82
## 9 DPR Mid 1019. 228. 1991 9 54.8 0 0.820
## 10 DPR Mid 1019. 228. 1991 10 37.8 0 119.
## # ℹ 86 more rows
## # ℹ 4 more variables: tmn <dbl>, tmx <dbl>, firstmonth <dbl>, lastmonth <dbl>#snow_last_month_col %>% filter(year==1991)
#snow_last_month_col %>% filter(parent.pop=="DPR") %>% filter(year==1962|year==1975)
#snow_last_month_col %>% filter(parent.pop=="WR") %>% filter(year==1975|year==1986)
#snow_last_month_col %>% filter(parent.pop=="YO7") %>% filter(year==1976)
#snow_last_month_col %>% filter(parent.pop=="FR") %>% filter(year==1975)
#snow_last_month_col %>% filter(parent.pop=="SQ3") %>% filter(year==1976)
snow_grwseason_historical <- snow_last_month_col %>% #fill in months b/t start and stop
group_by(parent.pop, elevation.group, elev_m, year) %>%
filter(month>=firstmonth) %>%
filter(ifelse(is.na(lastmonth), pck<70, month<lastmonth))
summary(snow_grwseason_historical) ## parent.pop elevation.group elev_m PckSum
## Length:3702 Length:3702 Min. : 748.9 Min. : 228.4
## Class :character Class :character 1st Qu.:1158.0 1st Qu.: 560.8
## Mode :character Mode :character Median :2020.1 Median :1776.3
## Mean :1844.7 Mean :2016.3
## 3rd Qu.:2373.2 3rd Qu.:3157.7
## Max. :2872.3 Max. :6363.1
##
## year month cwd pck
## Length:3702 Min. : 1.000 Min. : 0.00 Min. : 0.000
## Class :character 1st Qu.: 6.000 1st Qu.: 40.60 1st Qu.: 0.000
## Mode :character Median : 8.000 Median : 65.75 Median : 0.000
## Mean : 7.857 Mean : 65.78 Mean : 1.726
## 3rd Qu.:10.000 3rd Qu.: 88.92 3rd Qu.: 0.000
## Max. :12.000 Max. :159.40 Max. :69.880
##
## ppt tmn tmx firstmonth
## Min. : 0.000 Min. :-13.580 Min. : 0.82 Min. :1.000
## 1st Qu.: 5.965 1st Qu.: 0.620 1st Qu.:13.64 1st Qu.:3.000
## Median : 23.285 Median : 4.045 Median :19.02 Median :5.000
## Mean : 53.734 Mean : 4.120 Mean :18.65 Mean :4.518
## 3rd Qu.: 68.930 3rd Qu.: 7.800 3rd Qu.:22.97 3rd Qu.:6.000
## Max. :794.420 Max. : 16.840 Max. :35.12 Max. :9.000
##
## lastmonth
## Min. : 2.0
## 1st Qu.:11.0
## Median :12.0
## Mean :11.5
## 3rd Qu.:12.0
## Max. :12.0
## NA's :1721xtabs(~parent.pop+month, data=snow_grwseason_historical)## month
## parent.pop 1 2 3 4 5 6 7 8 9 10 11 12
## CP2 0 0 0 2 11 25 30 30 30 30 16 8
## CP3 0 0 0 1 9 24 30 30 30 30 15 8
## DPR 10 15 22 26 27 27 27 27 27 27 27 21
## FR 6 10 23 28 29 29 29 29 29 29 29 22
## LV1 0 0 0 0 0 13 25 29 30 28 7 1
## LV3 0 0 0 0 0 13 26 29 30 30 7 1
## LVTR1 0 0 0 0 0 13 25 29 30 28 7 1
## SQ1 2 3 2 14 26 29 29 29 29 29 29 15
## SQ2 1 3 2 14 26 29 29 29 29 29 29 16
## SQ3 1 1 0 3 14 26 29 29 29 29 22 11
## WL1 2 3 5 15 29 29 29 29 29 29 27 15
## WL2 1 2 1 6 15 29 29 29 29 29 22 8
## WR 7 6 13 23 27 27 27 27 27 27 26 16
## WV 4 8 17 29 30 30 30 30 30 30 30 19
## YO11 0 0 0 0 6 23 30 30 30 30 15 7
## YO4 1 2 1 10 18 29 29 29 29 29 22 9
## YO7 1 0 0 1 9 22 29 29 29 29 13 6
## YO8 0 0 0 1 8 22 30 30 30 30 14 7options(max.print=1000000)
xtabs(~year+month+parent.pop, data=snow_grwseason_historical)## , , parent.pop = CP2
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 0 0 1 1 1 1 1 1 1
## 1963 0 0 0 0 0 1 1 1 1 1 0 0
## 1964 0 0 0 0 0 1 1 1 1 1 0 0
## 1965 0 0 0 0 0 0 1 1 1 1 0 0
## 1966 0 0 0 0 1 1 1 1 1 1 1 0
## 1967 0 0 0 0 0 0 1 1 1 1 1 0
## 1968 0 0 0 0 1 1 1 1 1 1 0 0
## 1969 0 0 0 0 0 0 1 1 1 1 1 0
## 1970 0 0 0 0 0 1 1 1 1 1 0 0
## 1971 0 0 0 0 0 1 1 1 1 1 0 0
## 1972 0 0 0 0 1 1 1 1 1 1 0 0
## 1973 0 0 0 0 0 1 1 1 1 1 0 0
## 1974 0 0 0 0 0 1 1 1 1 1 1 0
## 1975 0 0 0 0 0 1 1 1 1 1 1 1
## 1976 0 0 0 0 1 1 1 1 1 1 1 1
## 1977 0 0 0 1 1 1 1 1 1 1 1 0
## 1978 0 0 0 0 0 1 1 1 1 1 1 0
## 1979 0 0 0 0 0 1 1 1 1 1 1 0
## 1980 0 0 0 0 0 1 1 1 1 1 1 1
## 1981 0 0 0 0 1 1 1 1 1 1 0 0
## 1982 0 0 0 0 0 0 1 1 1 1 0 0
## 1983 0 0 0 0 0 0 1 1 1 1 0 0
## 1984 0 0 0 0 0 1 1 1 1 1 0 0
## 1985 0 0 0 0 1 1 1 1 1 1 0 0
## 1986 0 0 0 0 0 1 1 1 1 1 1 1
## 1987 0 0 0 0 1 1 1 1 1 1 1 0
## 1988 0 0 0 0 1 1 1 1 1 1 0 0
## 1989 0 0 0 0 1 1 1 1 1 1 1 1
## 1990 0 0 0 1 1 1 1 1 1 1 1 1
## 1991 0 0 0 0 0 1 1 1 1 1 1 1
##
## , , parent.pop = CP3
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 0 0 1 1 1 1 1 1 1
## 1963 0 0 0 0 0 1 1 1 1 1 0 0
## 1964 0 0 0 0 0 1 1 1 1 1 0 0
## 1965 0 0 0 0 0 0 1 1 1 1 0 0
## 1966 0 0 0 0 1 1 1 1 1 1 0 0
## 1967 0 0 0 0 0 0 1 1 1 1 1 0
## 1968 0 0 0 0 1 1 1 1 1 1 0 0
## 1969 0 0 0 0 0 0 1 1 1 1 1 0
## 1970 0 0 0 0 0 1 1 1 1 1 0 0
## 1971 0 0 0 0 0 1 1 1 1 1 0 0
## 1972 0 0 0 0 0 1 1 1 1 1 0 0
## 1973 0 0 0 0 0 1 1 1 1 1 0 0
## 1974 0 0 0 0 0 1 1 1 1 1 1 0
## 1975 0 0 0 0 0 1 1 1 1 1 1 1
## 1976 0 0 0 0 1 1 1 1 1 1 1 1
## 1977 0 0 0 1 1 1 1 1 1 1 1 0
## 1978 0 0 0 0 0 1 1 1 1 1 1 0
## 1979 0 0 0 0 0 1 1 1 1 1 1 0
## 1980 0 0 0 0 0 0 1 1 1 1 1 1
## 1981 0 0 0 0 1 1 1 1 1 1 0 0
## 1982 0 0 0 0 0 0 1 1 1 1 0 0
## 1983 0 0 0 0 0 0 1 1 1 1 0 0
## 1984 0 0 0 0 0 1 1 1 1 1 0 0
## 1985 0 0 0 0 1 1 1 1 1 1 0 0
## 1986 0 0 0 0 0 1 1 1 1 1 1 1
## 1987 0 0 0 0 1 1 1 1 1 1 1 0
## 1988 0 0 0 0 1 1 1 1 1 1 0 0
## 1989 0 0 0 0 0 1 1 1 1 1 1 1
## 1990 0 0 0 0 1 1 1 1 1 1 1 1
## 1991 0 0 0 0 0 1 1 1 1 1 1 1
##
## , , parent.pop = DPR
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 1 0 0 0 0 0 0 0 0 0 0 0
## 1963 0 1 1 1 1 1 1 1 1 1 1 1
## 1964 0 1 1 1 1 1 1 1 1 1 1 0
## 1965 0 0 1 1 1 1 1 1 1 1 1 1
## 1966 0 0 1 1 1 1 1 1 1 1 1 1
## 1967 0 1 1 1 1 1 1 1 1 1 1 1
## 1968 0 0 1 1 1 1 1 1 1 1 1 0
## 1969 0 0 0 0 1 1 1 1 1 1 1 1
## 1970 0 0 1 1 1 1 1 1 1 1 1 0
## 1971 0 0 0 1 1 1 1 1 1 1 1 0
## 1972 0 0 1 1 1 1 1 1 1 1 1 0
## 1973 0 0 0 1 1 1 1 1 1 1 1 1
## 1974 0 0 1 1 1 1 1 1 1 1 1 1
## 1975 1 0 0 0 0 0 0 0 0 0 0 0
## 1976 1 1 1 1 1 1 1 1 1 1 1 1
## 1977 1 1 1 1 1 1 1 1 1 1 1 1
## 1978 0 1 1 1 1 1 1 1 1 1 1 1
## 1979 0 0 1 1 1 1 1 1 1 1 1 1
## 1980 0 1 1 1 1 1 1 1 1 1 1 1
## 1981 1 1 1 1 1 1 1 1 1 1 1 1
## 1982 0 0 0 1 1 1 1 1 1 1 1 1
## 1983 0 0 0 1 1 1 1 1 1 1 1 1
## 1984 1 1 1 1 1 1 1 1 1 1 1 1
## 1985 1 1 1 1 1 1 1 1 1 1 1 1
## 1986 1 1 1 1 1 1 1 1 1 1 1 1
## 1987 0 1 1 1 1 1 1 1 1 1 1 0
## 1988 0 1 1 1 1 1 1 1 1 1 1 1
## 1989 1 1 1 1 1 1 1 1 1 1 1 1
## 1990 0 0 1 1 1 1 1 1 1 1 1 1
## 1991 1 1 0 0 0 0 0 0 0 0 0 0
##
## , , parent.pop = FR
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 1 1 1 1 1 1 1 1 1
## 1963 0 1 1 1 1 1 1 1 1 1 1 1
## 1964 0 0 1 1 1 1 1 1 1 1 1 0
## 1965 0 0 1 1 1 1 1 1 1 1 1 1
## 1966 0 0 1 1 1 1 1 1 1 1 1 1
## 1967 0 0 0 1 1 1 1 1 1 1 1 1
## 1968 0 0 1 1 1 1 1 1 1 1 1 0
## 1969 0 0 0 0 1 1 1 1 1 1 1 1
## 1970 0 0 1 1 1 1 1 1 1 1 1 0
## 1971 0 0 0 1 1 1 1 1 1 1 1 0
## 1972 0 0 1 1 1 1 1 1 1 1 1 0
## 1973 0 0 0 1 1 1 1 1 1 1 1 1
## 1974 0 0 1 1 1 1 1 1 1 1 1 1
## 1975 1 0 0 0 0 0 0 0 0 0 0 0
## 1976 1 1 1 1 1 1 1 1 1 1 1 1
## 1977 0 1 1 1 1 1 1 1 1 1 1 1
## 1978 0 0 1 1 1 1 1 1 1 1 1 1
## 1979 0 0 1 1 1 1 1 1 1 1 1 1
## 1980 0 1 1 1 1 1 1 1 1 1 1 1
## 1981 0 1 1 1 1 1 1 1 1 1 1 1
## 1982 0 0 1 1 1 1 1 1 1 1 1 1
## 1983 0 0 0 1 1 1 1 1 1 1 1 0
## 1984 0 0 1 1 1 1 1 1 1 1 1 1
## 1985 1 1 1 1 1 1 1 1 1 1 1 1
## 1986 1 1 1 1 1 1 1 1 1 1 1 1
## 1987 0 0 1 1 1 1 1 1 1 1 1 0
## 1988 0 1 1 1 1 1 1 1 1 1 1 1
## 1989 1 1 1 1 1 1 1 1 1 1 1 1
## 1990 0 0 1 1 1 1 1 1 1 1 1 1
## 1991 1 1 1 1 1 1 1 1 1 1 1 1
##
## , , parent.pop = LV1
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 0 0 0 1 1 1 1 0 0
## 1963 0 0 0 0 0 0 1 1 1 1 0 0
## 1964 0 0 0 0 0 1 1 1 1 1 0 0
## 1965 0 0 0 0 0 0 1 1 1 1 0 0
## 1966 0 0 0 0 0 1 1 1 1 1 0 0
## 1967 0 0 0 0 0 0 0 1 1 1 1 0
## 1968 0 0 0 0 0 1 1 1 1 1 0 0
## 1969 0 0 0 0 0 0 1 1 1 1 1 0
## 1970 0 0 0 0 0 0 1 1 1 1 0 0
## 1971 0 0 0 0 0 0 1 1 1 1 0 0
## 1972 0 0 0 0 0 1 1 1 1 1 0 0
## 1973 0 0 0 0 0 0 1 1 1 1 0 0
## 1974 0 0 0 0 0 0 0 1 1 1 0 0
## 1975 0 0 0 0 0 0 1 1 1 1 0 0
## 1976 0 0 0 0 0 1 1 1 1 1 1 1
## 1977 0 0 0 0 0 1 1 1 1 1 0 0
## 1978 0 0 0 0 0 0 0 1 1 1 0 0
## 1979 0 0 0 0 0 1 1 1 1 1 0 0
## 1980 0 0 0 0 0 0 1 1 1 1 1 0
## 1981 0 0 0 0 0 1 1 1 1 0 0 0
## 1982 0 0 0 0 0 0 0 1 1 1 0 0
## 1983 0 0 0 0 0 0 0 0 1 1 0 0
## 1984 0 0 0 0 0 0 1 1 1 0 0 0
## 1985 0 0 0 0 0 1 1 1 1 1 0 0
## 1986 0 0 0 0 0 0 1 1 1 1 1 0
## 1987 0 0 0 0 0 1 1 1 1 1 0 0
## 1988 0 0 0 0 0 1 1 1 1 1 0 0
## 1989 0 0 0 0 0 0 1 1 1 1 0 0
## 1990 0 0 0 0 0 1 1 1 1 1 1 0
## 1991 0 0 0 0 0 1 1 1 1 1 1 0
##
## , , parent.pop = LV3
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 0 0 0 1 1 1 1 0 0
## 1963 0 0 0 0 0 0 1 1 1 1 0 0
## 1964 0 0 0 0 0 1 1 1 1 1 0 0
## 1965 0 0 0 0 0 0 1 1 1 1 0 0
## 1966 0 0 0 0 0 1 1 1 1 1 0 0
## 1967 0 0 0 0 0 0 0 1 1 1 1 0
## 1968 0 0 0 0 0 1 1 1 1 1 0 0
## 1969 0 0 0 0 0 0 1 1 1 1 1 0
## 1970 0 0 0 0 0 0 1 1 1 1 0 0
## 1971 0 0 0 0 0 0 1 1 1 1 0 0
## 1972 0 0 0 0 0 1 1 1 1 1 0 0
## 1973 0 0 0 0 0 0 1 1 1 1 0 0
## 1974 0 0 0 0 0 0 0 1 1 1 0 0
## 1975 0 0 0 0 0 0 1 1 1 1 0 0
## 1976 0 0 0 0 0 1 1 1 1 1 1 1
## 1977 0 0 0 0 0 1 1 1 1 1 0 0
## 1978 0 0 0 0 0 0 1 1 1 1 0 0
## 1979 0 0 0 0 0 1 1 1 1 1 0 0
## 1980 0 0 0 0 0 0 1 1 1 1 1 0
## 1981 0 0 0 0 0 1 1 1 1 1 0 0
## 1982 0 0 0 0 0 0 0 1 1 1 0 0
## 1983 0 0 0 0 0 0 0 0 1 1 0 0
## 1984 0 0 0 0 0 0 1 1 1 1 0 0
## 1985 0 0 0 0 0 1 1 1 1 1 0 0
## 1986 0 0 0 0 0 0 1 1 1 1 1 0
## 1987 0 0 0 0 0 1 1 1 1 1 0 0
## 1988 0 0 0 0 0 1 1 1 1 1 0 0
## 1989 0 0 0 0 0 0 1 1 1 1 0 0
## 1990 0 0 0 0 0 1 1 1 1 1 1 0
## 1991 0 0 0 0 0 1 1 1 1 1 1 0
##
## , , parent.pop = LVTR1
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 0 0 0 1 1 1 1 0 0
## 1963 0 0 0 0 0 0 1 1 1 1 0 0
## 1964 0 0 0 0 0 1 1 1 1 1 0 0
## 1965 0 0 0 0 0 0 1 1 1 1 0 0
## 1966 0 0 0 0 0 1 1 1 1 1 0 0
## 1967 0 0 0 0 0 0 0 1 1 1 1 0
## 1968 0 0 0 0 0 1 1 1 1 1 0 0
## 1969 0 0 0 0 0 0 1 1 1 1 1 0
## 1970 0 0 0 0 0 0 1 1 1 1 0 0
## 1971 0 0 0 0 0 0 1 1 1 1 0 0
## 1972 0 0 0 0 0 1 1 1 1 1 0 0
## 1973 0 0 0 0 0 0 1 1 1 1 0 0
## 1974 0 0 0 0 0 0 0 1 1 1 0 0
## 1975 0 0 0 0 0 0 1 1 1 1 0 0
## 1976 0 0 0 0 0 1 1 1 1 1 1 1
## 1977 0 0 0 0 0 1 1 1 1 1 0 0
## 1978 0 0 0 0 0 0 0 1 1 1 0 0
## 1979 0 0 0 0 0 1 1 1 1 1 0 0
## 1980 0 0 0 0 0 0 1 1 1 1 1 0
## 1981 0 0 0 0 0 1 1 1 1 0 0 0
## 1982 0 0 0 0 0 0 0 1 1 1 0 0
## 1983 0 0 0 0 0 0 0 0 1 1 0 0
## 1984 0 0 0 0 0 0 1 1 1 0 0 0
## 1985 0 0 0 0 0 1 1 1 1 1 0 0
## 1986 0 0 0 0 0 0 1 1 1 1 1 0
## 1987 0 0 0 0 0 1 1 1 1 1 0 0
## 1988 0 0 0 0 0 1 1 1 1 1 0 0
## 1989 0 0 0 0 0 0 1 1 1 1 0 0
## 1990 0 0 0 0 0 1 1 1 1 1 1 0
## 1991 0 0 0 0 0 1 1 1 1 1 1 0
##
## , , parent.pop = SQ1
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 0 1 1 1 1 1 1 1 1
## 1963 0 0 0 0 1 1 1 1 1 1 1 1
## 1964 0 0 0 1 1 1 1 1 1 1 1 0
## 1965 0 0 0 0 1 1 1 1 1 1 1 0
## 1966 0 0 0 1 1 1 1 1 1 1 1 0
## 1967 0 0 0 0 0 1 1 1 1 1 1 0
## 1968 0 0 0 1 1 1 1 1 1 1 1 0
## 1969 0 0 0 0 0 1 1 1 1 1 1 1
## 1970 0 0 0 0 1 1 1 1 1 1 1 0
## 1971 0 0 0 0 1 1 1 1 1 1 1 0
## 1972 0 0 0 1 1 1 1 1 1 1 1 0
## 1973 0 0 0 0 1 1 1 1 1 1 1 1
## 1974 0 0 0 0 1 1 1 1 1 1 1 1
## 1975 0 0 0 0 1 1 1 1 1 1 1 1
## 1976 1 1 1 1 1 1 1 1 1 1 1 1
## 1977 0 1 1 1 1 1 1 1 1 1 1 1
## 1978 0 0 0 0 1 1 1 1 1 1 1 0
## 1979 0 0 0 0 1 1 1 1 1 1 1 1
## 1980 0 0 0 0 1 1 1 1 1 1 1 1
## 1981 0 0 0 1 1 1 1 1 1 1 1 1
## 1982 0 0 0 0 1 1 1 1 1 1 1 0
## 1983 0 0 0 0 0 1 1 1 1 1 1 0
## 1984 0 0 0 1 1 1 1 1 1 1 1 0
## 1985 0 0 0 1 1 1 1 1 1 1 1 1
## 1986 0 0 0 1 1 1 1 1 1 1 1 1
## 1987 0 0 0 1 1 1 1 1 1 1 1 0
## 1988 0 0 0 1 1 1 1 1 1 1 1 0
## 1989 0 0 0 1 1 1 1 1 1 1 1 1
## 1990 0 0 0 1 1 1 1 1 1 1 1 1
## 1991 1 1 0 0 0 0 0 0 0 0 0 0
##
## , , parent.pop = SQ2
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 0 1 1 1 1 1 1 1 1
## 1963 0 0 0 0 1 1 1 1 1 1 1 1
## 1964 0 0 0 1 1 1 1 1 1 1 1 0
## 1965 0 0 0 0 1 1 1 1 1 1 1 0
## 1966 0 0 0 1 1 1 1 1 1 1 1 0
## 1967 0 0 0 0 0 1 1 1 1 1 1 0
## 1968 0 0 0 1 1 1 1 1 1 1 1 0
## 1969 0 0 0 0 0 1 1 1 1 1 1 1
## 1970 0 0 0 0 1 1 1 1 1 1 1 0
## 1971 0 0 0 0 1 1 1 1 1 1 1 0
## 1972 0 0 0 1 1 1 1 1 1 1 1 0
## 1973 0 0 0 0 1 1 1 1 1 1 1 1
## 1974 0 0 0 0 1 1 1 1 1 1 1 1
## 1975 0 0 0 0 1 1 1 1 1 1 1 1
## 1976 1 1 1 1 1 1 1 1 1 1 1 1
## 1977 0 1 1 1 1 1 1 1 1 1 1 1
## 1978 0 0 0 0 1 1 1 1 1 1 1 1
## 1979 0 0 0 0 1 1 1 1 1 1 1 1
## 1980 0 0 0 0 1 1 1 1 1 1 1 1
## 1981 0 0 0 1 1 1 1 1 1 1 1 1
## 1982 0 0 0 0 1 1 1 1 1 1 1 0
## 1983 0 0 0 0 0 1 1 1 1 1 1 0
## 1984 0 0 0 1 1 1 1 1 1 1 1 0
## 1985 0 0 0 1 1 1 1 1 1 1 1 1
## 1986 0 0 0 1 1 1 1 1 1 1 1 1
## 1987 0 0 0 1 1 1 1 1 1 1 1 0
## 1988 0 0 0 1 1 1 1 1 1 1 1 0
## 1989 0 0 0 1 1 1 1 1 1 1 1 1
## 1990 0 0 0 1 1 1 1 1 1 1 1 1
## 1991 0 1 0 0 0 0 0 0 0 0 0 0
##
## , , parent.pop = SQ3
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 0 0 1 1 1 1 1 1 1
## 1963 0 0 0 0 0 1 1 1 1 1 1 1
## 1964 0 0 0 0 1 1 1 1 1 1 0 0
## 1965 0 0 0 0 0 1 1 1 1 1 1 0
## 1966 0 0 0 0 1 1 1 1 1 1 1 0
## 1967 0 0 0 0 0 0 1 1 1 1 1 0
## 1968 0 0 0 0 1 1 1 1 1 1 1 0
## 1969 0 0 0 0 0 0 1 1 1 1 1 1
## 1970 0 0 0 0 1 1 1 1 1 1 1 0
## 1971 0 0 0 0 0 1 1 1 1 1 1 0
## 1972 0 0 0 0 1 1 1 1 1 1 0 0
## 1973 0 0 0 0 0 1 1 1 1 1 0 0
## 1974 0 0 0 0 0 1 1 1 1 1 1 0
## 1975 0 0 0 0 0 1 1 1 1 1 1 1
## 1976 1 1 0 0 0 0 0 0 0 0 0 0
## 1977 0 0 0 1 1 1 1 1 1 1 1 0
## 1978 0 0 0 0 0 1 1 1 1 1 1 0
## 1979 0 0 0 0 0 1 1 1 1 1 1 1
## 1980 0 0 0 0 0 1 1 1 1 1 1 1
## 1981 0 0 0 0 1 1 1 1 1 1 1 1
## 1982 0 0 0 0 0 1 1 1 1 1 0 0
## 1983 0 0 0 0 0 0 1 1 1 1 0 0
## 1984 0 0 0 0 1 1 1 1 1 1 0 0
## 1985 0 0 0 0 1 1 1 1 1 1 0 0
## 1986 0 0 0 0 1 1 1 1 1 1 1 1
## 1987 0 0 0 0 1 1 1 1 1 1 1 0
## 1988 0 0 0 0 1 1 1 1 1 1 1 0
## 1989 0 0 0 1 1 1 1 1 1 1 1 1
## 1990 0 0 0 1 1 1 1 1 1 1 1 1
## 1991 0 0 0 0 0 1 1 1 1 1 1 1
##
## , , parent.pop = WL1
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 0 1 1 1 1 1 1 1 1
## 1963 0 0 0 0 1 1 1 1 1 1 1 1
## 1964 0 0 0 1 1 1 1 1 1 1 1 0
## 1965 0 0 0 0 1 1 1 1 1 1 1 0
## 1966 0 0 0 1 1 1 1 1 1 1 1 1
## 1967 0 0 0 0 1 1 1 1 1 1 1 0
## 1968 0 0 0 1 1 1 1 1 1 1 1 0
## 1969 0 0 0 0 1 1 1 1 1 1 1 0
## 1970 0 0 0 0 1 1 1 1 1 1 1 0
## 1971 0 0 0 0 1 1 1 1 1 1 1 0
## 1972 0 0 0 1 1 1 1 1 1 1 1 0
## 1973 0 0 0 0 1 1 1 1 1 1 0 0
## 1974 0 0 0 0 1 1 1 1 1 1 1 1
## 1975 0 0 0 0 1 1 1 1 1 1 1 1
## 1976 1 1 1 1 1 1 1 1 1 1 1 1
## 1977 0 1 1 1 1 1 1 1 1 1 1 1
## 1978 0 0 0 0 1 1 1 1 1 1 1 1
## 1979 0 0 0 0 1 1 1 1 1 1 1 1
## 1980 0 0 0 1 1 1 1 1 1 1 1 1
## 1981 0 0 1 1 1 1 1 1 1 1 1 0
## 1982 0 0 0 0 1 1 1 1 1 1 1 0
## 1983 0 0 0 0 1 1 1 1 1 1 0 0
## 1984 0 0 0 1 1 1 1 1 1 1 1 0
## 1985 0 0 0 1 1 1 1 1 1 1 1 1
## 1986 0 0 0 1 1 1 1 1 1 1 1 1
## 1987 0 0 0 1 1 1 1 1 1 1 1 0
## 1988 0 0 1 1 1 1 1 1 1 1 1 1
## 1989 0 0 0 1 1 1 1 1 1 1 1 1
## 1990 0 0 1 1 1 1 1 1 1 1 1 1
## 1991 1 1 0 0 0 0 0 0 0 0 0 0
##
## , , parent.pop = WL2
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 0 0 1 1 1 1 1 1 1
## 1963 0 0 0 0 1 1 1 1 1 1 0 0
## 1964 0 0 0 0 1 1 1 1 1 1 0 0
## 1965 0 0 0 0 0 1 1 1 1 1 1 0
## 1966 0 0 0 0 1 1 1 1 1 1 1 0
## 1967 0 0 0 0 0 1 1 1 1 1 1 0
## 1968 0 0 0 0 1 1 1 1 1 1 1 0
## 1969 0 0 0 0 0 1 1 1 1 1 1 0
## 1970 0 0 0 0 0 1 1 1 1 1 1 0
## 1971 0 0 0 0 0 1 1 1 1 1 1 0
## 1972 0 0 0 0 1 1 1 1 1 1 1 0
## 1973 0 0 0 0 0 1 1 1 1 1 0 0
## 1974 0 0 0 0 0 1 1 1 1 1 1 0
## 1975 0 0 0 0 0 1 1 1 1 1 1 1
## 1976 1 1 1 1 1 1 1 1 1 1 1 1
## 1977 0 0 0 1 1 1 1 1 1 1 1 0
## 1978 0 0 0 0 0 1 1 1 1 1 1 0
## 1979 0 0 0 0 1 1 1 1 1 1 1 1
## 1980 0 0 0 0 0 1 1 1 1 1 1 1
## 1981 0 0 0 1 1 1 1 1 1 1 1 0
## 1982 0 0 0 0 0 1 1 1 1 1 0 0
## 1983 0 0 0 0 0 1 1 1 1 1 0 0
## 1984 0 0 0 0 0 1 1 1 1 1 0 0
## 1985 0 0 0 0 1 1 1 1 1 1 0 0
## 1986 0 0 0 0 1 1 1 1 1 1 1 1
## 1987 0 0 0 1 1 1 1 1 1 1 1 0
## 1988 0 0 0 1 1 1 1 1 1 1 1 0
## 1989 0 0 0 0 1 1 1 1 1 1 1 1
## 1990 0 0 0 1 1 1 1 1 1 1 1 1
## 1991 0 1 0 0 0 0 0 0 0 0 0 0
##
## , , parent.pop = WR
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 1 1 1 1 1 1 1 1 1
## 1963 0 1 1 1 1 1 1 1 1 1 1 1
## 1964 0 0 1 1 1 1 1 1 1 1 1 0
## 1965 0 0 0 1 1 1 1 1 1 1 1 0
## 1966 0 0 0 1 1 1 1 1 1 1 1 1
## 1967 0 0 0 0 1 1 1 1 1 1 1 0
## 1968 0 0 1 1 1 1 1 1 1 1 1 0
## 1969 0 0 0 0 1 1 1 1 1 1 1 1
## 1970 0 0 0 1 1 1 1 1 1 1 1 0
## 1971 0 0 0 1 1 1 1 1 1 1 1 0
## 1972 0 0 0 1 1 1 1 1 1 1 1 0
## 1973 0 0 0 0 1 1 1 1 1 1 0 0
## 1974 0 0 0 1 1 1 1 1 1 1 1 1
## 1975 1 0 0 0 0 0 0 0 0 0 0 0
## 1976 1 1 1 1 1 1 1 1 1 1 1 1
## 1977 1 1 1 1 1 1 1 1 1 1 1 1
## 1978 0 0 1 1 1 1 1 1 1 1 1 1
## 1979 0 0 0 1 1 1 1 1 1 1 1 1
## 1980 0 0 0 1 1 1 1 1 1 1 1 1
## 1981 1 1 1 1 1 1 1 1 1 1 1 1
## 1982 0 0 0 1 1 1 1 1 1 1 1 0
## 1983 0 0 0 0 1 1 1 1 1 1 1 0
## 1984 0 0 1 1 1 1 1 1 1 1 1 1
## 1985 1 1 1 1 1 1 1 1 1 1 1 1
## 1986 1 0 0 0 0 0 0 0 0 0 0 0
## 1987 0 0 1 1 1 1 1 1 1 1 1 0
## 1988 0 0 1 1 1 1 1 1 1 1 1 1
## 1989 0 0 1 1 1 1 1 1 1 1 1 1
## 1990 0 0 1 1 1 1 1 1 1 1 1 1
## 1991 1 1 0 0 0 0 0 0 0 0 0 0
##
## , , parent.pop = WV
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 1 1 1 1 1 1 1 1 1 1
## 1963 0 1 1 1 1 1 1 1 1 1 1 1
## 1964 0 0 1 1 1 1 1 1 1 1 1 0
## 1965 0 0 0 1 1 1 1 1 1 1 1 1
## 1966 0 0 0 1 1 1 1 1 1 1 1 1
## 1967 0 0 0 1 1 1 1 1 1 1 1 0
## 1968 0 0 0 1 1 1 1 1 1 1 1 0
## 1969 0 0 0 0 1 1 1 1 1 1 1 0
## 1970 0 0 0 1 1 1 1 1 1 1 1 0
## 1971 0 0 0 1 1 1 1 1 1 1 1 0
## 1972 0 0 0 1 1 1 1 1 1 1 1 0
## 1973 0 0 0 1 1 1 1 1 1 1 1 1
## 1974 0 0 0 1 1 1 1 1 1 1 1 1
## 1975 0 0 0 1 1 1 1 1 1 1 1 1
## 1976 1 1 1 1 1 1 1 1 1 1 1 1
## 1977 0 1 1 1 1 1 1 1 1 1 1 1
## 1978 0 0 1 1 1 1 1 1 1 1 1 1
## 1979 0 0 1 1 1 1 1 1 1 1 1 1
## 1980 0 1 1 1 1 1 1 1 1 1 1 1
## 1981 1 1 1 1 1 1 1 1 1 1 1 1
## 1982 0 0 0 1 1 1 1 1 1 1 1 0
## 1983 0 0 0 1 1 1 1 1 1 1 1 0
## 1984 0 0 1 1 1 1 1 1 1 1 1 0
## 1985 0 1 1 1 1 1 1 1 1 1 1 1
## 1986 1 1 1 1 1 1 1 1 1 1 1 1
## 1987 0 0 1 1 1 1 1 1 1 1 1 0
## 1988 0 0 1 1 1 1 1 1 1 1 1 1
## 1989 0 0 1 1 1 1 1 1 1 1 1 1
## 1990 0 0 1 1 1 1 1 1 1 1 1 1
## 1991 1 1 1 1 1 1 1 1 1 1 1 1
##
## , , parent.pop = YO11
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 0 0 1 1 1 1 1 1 1
## 1963 0 0 0 0 0 1 1 1 1 1 0 0
## 1964 0 0 0 0 0 1 1 1 1 1 0 0
## 1965 0 0 0 0 0 0 1 1 1 1 0 0
## 1966 0 0 0 0 1 1 1 1 1 1 0 0
## 1967 0 0 0 0 0 0 1 1 1 1 1 0
## 1968 0 0 0 0 1 1 1 1 1 1 1 0
## 1969 0 0 0 0 0 0 1 1 1 1 1 0
## 1970 0 0 0 0 0 1 1 1 1 1 0 0
## 1971 0 0 0 0 0 1 1 1 1 1 0 0
## 1972 0 0 0 0 0 1 1 1 1 1 0 0
## 1973 0 0 0 0 0 1 1 1 1 1 0 0
## 1974 0 0 0 0 0 1 1 1 1 1 1 0
## 1975 0 0 0 0 0 1 1 1 1 1 1 1
## 1976 0 0 0 0 1 1 1 1 1 1 1 1
## 1977 0 0 0 0 1 1 1 1 1 1 1 0
## 1978 0 0 0 0 0 0 1 1 1 1 0 0
## 1979 0 0 0 0 0 1 1 1 1 1 1 0
## 1980 0 0 0 0 0 0 1 1 1 1 1 1
## 1981 0 0 0 0 0 1 1 1 1 1 0 0
## 1982 0 0 0 0 0 0 1 1 1 1 0 0
## 1983 0 0 0 0 0 0 1 1 1 1 0 0
## 1984 0 0 0 0 0 1 1 1 1 1 0 0
## 1985 0 0 0 0 0 1 1 1 1 1 0 0
## 1986 0 0 0 0 0 1 1 1 1 1 1 1
## 1987 0 0 0 0 1 1 1 1 1 1 1 0
## 1988 0 0 0 0 0 1 1 1 1 1 0 0
## 1989 0 0 0 0 0 1 1 1 1 1 1 1
## 1990 0 0 0 0 1 1 1 1 1 1 1 1
## 1991 0 0 0 0 0 1 1 1 1 1 1 0
##
## , , parent.pop = YO4
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 0 0 1 1 1 1 1 1 1
## 1963 0 0 0 0 1 1 1 1 1 1 1 1
## 1964 0 0 0 1 1 1 1 1 1 1 0 0
## 1965 0 0 0 0 0 1 1 1 1 1 0 0
## 1966 0 0 0 1 1 1 1 1 1 1 1 0
## 1967 0 0 0 0 0 1 1 1 1 1 1 0
## 1968 0 0 0 1 1 1 1 1 1 1 1 0
## 1969 0 0 0 0 0 1 1 1 1 1 1 0
## 1970 0 0 0 0 1 1 1 1 1 1 1 0
## 1971 0 0 0 0 1 1 1 1 1 1 1 0
## 1972 0 0 0 0 1 1 1 1 1 1 1 0
## 1973 0 0 0 0 0 1 1 1 1 1 0 0
## 1974 0 0 0 0 1 1 1 1 1 1 1 0
## 1975 0 0 0 0 0 1 1 1 1 1 1 1
## 1976 1 1 1 1 1 1 1 1 1 1 1 1
## 1977 0 0 0 1 1 1 1 1 1 1 1 0
## 1978 0 0 0 0 0 1 1 1 1 1 1 0
## 1979 0 0 0 0 0 1 1 1 1 1 1 1
## 1980 0 0 0 0 0 1 1 1 1 1 1 1
## 1981 0 0 0 1 1 1 1 1 1 1 1 0
## 1982 0 0 0 0 0 1 1 1 1 1 0 0
## 1983 0 0 0 0 0 1 1 1 1 1 0 0
## 1984 0 0 0 0 1 1 1 1 1 1 0 0
## 1985 0 0 0 0 1 1 1 1 1 1 0 0
## 1986 0 0 0 0 1 1 1 1 1 1 1 1
## 1987 0 0 0 1 1 1 1 1 1 1 1 0
## 1988 0 0 0 1 1 1 1 1 1 1 1 0
## 1989 0 0 0 1 1 1 1 1 1 1 1 1
## 1990 0 0 0 1 1 1 1 1 1 1 1 1
## 1991 0 1 0 0 0 0 0 0 0 0 0 0
##
## , , parent.pop = YO7
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 0 0 1 1 1 1 1 1 1
## 1963 0 0 0 0 0 1 1 1 1 1 0 0
## 1964 0 0 0 0 0 1 1 1 1 1 0 0
## 1965 0 0 0 0 0 0 1 1 1 1 0 0
## 1966 0 0 0 0 1 1 1 1 1 1 0 0
## 1967 0 0 0 0 0 0 1 1 1 1 1 0
## 1968 0 0 0 0 1 1 1 1 1 1 0 0
## 1969 0 0 0 0 0 0 1 1 1 1 1 0
## 1970 0 0 0 0 0 1 1 1 1 1 0 0
## 1971 0 0 0 0 0 1 1 1 1 1 0 0
## 1972 0 0 0 0 0 1 1 1 1 1 0 0
## 1973 0 0 0 0 0 1 1 1 1 1 0 0
## 1974 0 0 0 0 0 1 1 1 1 1 1 0
## 1975 0 0 0 0 0 1 1 1 1 1 1 1
## 1976 1 0 0 0 0 0 0 0 0 0 0 0
## 1977 0 0 0 1 1 1 1 1 1 1 1 0
## 1978 0 0 0 0 0 0 1 1 1 1 0 0
## 1979 0 0 0 0 0 1 1 1 1 1 1 0
## 1980 0 0 0 0 0 0 1 1 1 1 1 1
## 1981 0 0 0 0 1 1 1 1 1 1 0 0
## 1982 0 0 0 0 0 0 1 1 1 1 0 0
## 1983 0 0 0 0 0 0 1 1 1 1 0 0
## 1984 0 0 0 0 0 1 1 1 1 1 0 0
## 1985 0 0 0 0 1 1 1 1 1 1 0 0
## 1986 0 0 0 0 0 1 1 1 1 1 1 1
## 1987 0 0 0 0 1 1 1 1 1 1 1 0
## 1988 0 0 0 0 1 1 1 1 1 1 0 0
## 1989 0 0 0 0 1 1 1 1 1 1 1 1
## 1990 0 0 0 0 1 1 1 1 1 1 1 1
## 1991 0 0 0 0 0 1 1 1 1 1 1 0
##
## , , parent.pop = YO8
##
## month
## year 1 2 3 4 5 6 7 8 9 10 11 12
## 1962 0 0 0 0 0 1 1 1 1 1 1 1
## 1963 0 0 0 0 0 1 1 1 1 1 0 0
## 1964 0 0 0 0 0 1 1 1 1 1 0 0
## 1965 0 0 0 0 0 0 1 1 1 1 0 0
## 1966 0 0 0 0 1 1 1 1 1 1 0 0
## 1967 0 0 0 0 0 0 1 1 1 1 1 0
## 1968 0 0 0 0 1 1 1 1 1 1 0 0
## 1969 0 0 0 0 0 0 1 1 1 1 1 0
## 1970 0 0 0 0 0 1 1 1 1 1 0 0
## 1971 0 0 0 0 0 1 1 1 1 1 0 0
## 1972 0 0 0 0 0 1 1 1 1 1 0 0
## 1973 0 0 0 0 0 1 1 1 1 1 0 0
## 1974 0 0 0 0 0 1 1 1 1 1 1 0
## 1975 0 0 0 0 0 0 1 1 1 1 1 1
## 1976 0 0 0 0 1 1 1 1 1 1 1 1
## 1977 0 0 0 1 1 1 1 1 1 1 1 0
## 1978 0 0 0 0 0 0 1 1 1 1 0 0
## 1979 0 0 0 0 0 1 1 1 1 1 1 0
## 1980 0 0 0 0 0 0 1 1 1 1 1 1
## 1981 0 0 0 0 0 1 1 1 1 1 0 0
## 1982 0 0 0 0 0 0 1 1 1 1 0 0
## 1983 0 0 0 0 0 0 1 1 1 1 0 0
## 1984 0 0 0 0 0 1 1 1 1 1 0 0
## 1985 0 0 0 0 1 1 1 1 1 1 0 0
## 1986 0 0 0 0 0 1 1 1 1 1 1 1
## 1987 0 0 0 0 1 1 1 1 1 1 1 0
## 1988 0 0 0 0 1 1 1 1 1 1 0 0
## 1989 0 0 0 0 0 1 1 1 1 1 1 1
## 1990 0 0 0 0 1 1 1 1 1 1 1 1
## 1991 0 0 0 0 0 1 1 1 1 1 1 0snow_grwseason_historical %>% ggplot(aes(x=month)) + geom_histogram() +
scale_x_continuous(breaks=c(1, 2, 3, 4, 5,6,7,8,9,10, 11, 12)) +
facet_wrap(~parent.pop)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Bind snow and no_snow pops together
allpops_recent_grwseason <- rbind(nosnow_grwseason_recent, snow_grwseason_recent)
summary(allpops_recent_grwseason)## month growmonth parent.pop elevation.group
## Min. : 1.000 Min. : 1.000 Length:5215 Length:5215
## 1st Qu.: 5.000 1st Qu.: 5.000 Class :character Class :character
## Median : 7.000 Median : 7.000 Mode :character Mode :character
## Mean : 7.277 Mean : 7.018
## 3rd Qu.:10.000 3rd Qu.: 9.000
## Max. :12.000 Max. :12.000
## NA's :4000
## elev_m PckSum year cwd
## Min. : 313.0 Min. : 0.0 Length:5215 Min. : 0.00
## 1st Qu.: 748.9 1st Qu.: 107.5 Class :character 1st Qu.: 30.89
## Median :1613.8 Median : 613.4 Mode :character Median : 58.96
## Mean :1518.4 Mean :1268.3 Mean : 60.67
## 3rd Qu.:2266.4 3rd Qu.:2162.0 3rd Qu.: 86.77
## Max. :2872.3 Max. :5408.1 Max. :182.70
##
## pck ppt tmn tmx
## Min. : 0.000 Min. : 0.00 Min. :-10.440 Min. : 2.09
## 1st Qu.: 0.000 1st Qu.: 4.68 1st Qu.: 2.260 1st Qu.:13.77
## Median : 0.000 Median : 29.23 Median : 5.430 Median :18.91
## Mean : 1.091 Mean : 64.80 Mean : 5.666 Mean :19.02
## 3rd Qu.: 0.000 3rd Qu.: 89.72 3rd Qu.: 9.185 3rd Qu.:23.61
## Max. :69.930 Max. :803.25 Max. : 19.740 Max. :35.97
##
## firstmonth lastmonth
## Min. :1.000 Min. : 1.0
## 1st Qu.:3.000 1st Qu.:11.0
## Median :4.000 Median :12.0
## Mean :3.923 Mean :10.5
## 3rd Qu.:5.000 3rd Qu.:12.0
## Max. :8.000 Max. :12.0
## NA's :1780unique(allpops_recent_grwseason$parent.pop)## [1] "BH" "CC" "IH" "SC" "TM2" "CP2" "CP3" "DPR" "FR"
## [10] "LV1" "LV3" "LVTR1" "SQ1" "SQ2" "SQ3" "WL1" "WL2" "WR"
## [19] "WV" "YO11" "YO4" "YO7" "YO8"allpops_historical_grwseason <- rbind(nosnow_grwseason_historical, snow_grwseason_historical)
summary(allpops_historical_grwseason)## month growmonth parent.pop elevation.group
## Min. : 1.000 Min. : 1.000 Length:5012 Length:5012
## 1st Qu.: 5.000 1st Qu.: 5.000 Class :character Class :character
## Median : 8.000 Median : 7.000 Mode :character Mode :character
## Mean : 7.384 Mean : 6.724
## 3rd Qu.:10.000 3rd Qu.: 9.000
## Max. :12.000 Max. :12.000
## NA's :3702
## elev_m PckSum year cwd
## Min. : 313.0 Min. : 0.234 Length:5012 Min. : 0.00
## 1st Qu.: 511.4 1st Qu.: 17.344 Class :character 1st Qu.: 31.74
## Median :1613.8 Median : 891.549 Mode :character Median : 59.49
## Mean :1471.1 Mean :1490.692 Mean : 59.55
## 3rd Qu.:2266.4 3rd Qu.:2451.753 3rd Qu.: 83.22
## Max. :2872.3 Max. :6363.078 Max. :168.60
##
## pck ppt tmn tmx
## Min. : 0.000 Min. : 0.00 Min. :-13.580 Min. : 0.82
## 1st Qu.: 0.000 1st Qu.: 8.18 1st Qu.: 1.270 1st Qu.:13.80
## Median : 0.000 Median : 32.84 Median : 4.430 Median :18.68
## Mean : 1.422 Mean : 66.10 Mean : 4.582 Mean :18.75
## 3rd Qu.: 0.000 3rd Qu.: 87.59 3rd Qu.: 8.030 3rd Qu.:23.18
## Max. :129.460 Max. :794.42 Max. : 17.920 Max. :35.85
##
## firstmonth lastmonth
## Min. :1.00 Min. : 1.00
## 1st Qu.:3.00 1st Qu.:11.00
## Median :4.00 Median :11.00
## Mean :4.06 Mean :10.29
## 3rd Qu.:5.00 3rd Qu.:12.00
## Max. :9.00 Max. :12.00
## NA's :1721unique(allpops_historical_grwseason$parent.pop)## [1] "BH" "CC" "IH" "SC" "TM2" "CP2" "CP3" "DPR" "FR"
## [10] "LV1" "LV3" "LVTR1" "SQ1" "SQ2" "SQ3" "WL1" "WL2" "WR"
## [19] "WV" "YO11" "YO4" "YO7" "YO8"Climate traits across pops
Totals
names(allpops_recent_grwseason)## [1] "month" "growmonth" "parent.pop" "elevation.group"
## [5] "elev_m" "PckSum" "year" "cwd"
## [9] "pck" "ppt" "tmn" "tmx"
## [13] "firstmonth" "lastmonth"allpops_recent_grwseason_yearlytot <- allpops_recent_grwseason %>% group_by(parent.pop, year, elevation.group) %>% summarise_at(c("pck", "ppt"), sum, na.rm = TRUE)
allpops_recent_grwseason_yearlytot$elevation.group <- factor(allpops_recent_grwseason_yearlytot$elevation.group, levels = elev_order)
recent_ppt_total <- allpops_recent_grwseason_yearlytot %>% ggplot(aes(x=year, y=ppt, group=parent.pop, color=elevation.group)) +
geom_point() + geom_line() +
scale_colour_manual(values=elev_three_palette) +
ggtitle("Recent Climate") +
theme_classic() +
theme(text=element_text(size=30), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/growthseasonTot_Precip_RecentClim.png", width = 12, height = 6, units = "in")
allpops_historical_grwseason_yearlytot <- allpops_historical_grwseason %>% group_by(parent.pop, year, elevation.group) %>% summarise_at(c("pck", "ppt"), sum, na.rm = TRUE)
allpops_historical_grwseason_yearlytot$elevation.group <- factor(allpops_historical_grwseason_yearlytot$elevation.group, levels = elev_order)
hist_ppt_total <- allpops_historical_grwseason_yearlytot %>% ggplot(aes(x=year, y=ppt, group=parent.pop, color=elevation.group)) +
geom_point() + geom_line() +
scale_colour_manual(values=elev_three_palette) +
ggtitle("Historical Climate") +
theme_classic() +
theme(text=element_text(size=30), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/growthseasonTot_Precip_HistoricalClim.png", width = 12, height = 6, units = "in")
#should combine these into one figure and save that instead
legend <- get_legend(hist_ppt_total)
hist_ppt_total <- hist_ppt_total + theme(legend.position="none")
recent_ppt_total <- recent_ppt_total + theme(legend.position="none")
grid.arrange(hist_ppt_total, recent_ppt_total, legend, ncol=3, widths=c(3.12, 3.12, 1.09))
#2000 x 850Averages
Across last 30 years (all months included)
pop_elev_climate_avgs <- pop_elev_climate %>% filter(year>1992) %>% group_by(parent.pop, elevation.group) %>% summarise_at(c("cwd", "pck", "ppt", "tmn", "tmx"), mean, na.rm = TRUE)
pop_elev_climate_avgs #30 year averages of all climate variables ## # A tibble: 23 × 7
## # Groups: parent.pop [23]
## parent.pop elevation.group cwd pck ppt tmn tmx
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 75.9 0 47.8 8.88 23.6
## 2 CC Low 59.7 0 84.5 10.0 23.3
## 3 CP2 High 63.1 206. 105. 1.17 13.5
## 4 CP3 High 46.4 224. 100. 0.521 12.7
## 5 DPR Mid 27.4 8.70 121. 7.86 20.3
## 6 FR Mid 56.0 19.0 82.3 5.36 20.0
## 7 IH Low 49.1 0.184 88.7 8.67 22.3
## 8 LV1 High 49.8 440. 147. -1.38 11.2
## 9 LV3 High 57.6 426. 146. -1.36 11.2
## 10 LVTR1 High 52.0 453. 152. -1.58 11.2
## # ℹ 13 more rowspop_elev_climate_avgs$elevation.group <- factor(pop_elev_climate_avgs$elevation.group, levels=elev_order)Recent Years - Growth Season
allpops_recent_grwseason_avgs <- allpops_recent_grwseason %>% group_by(parent.pop, elevation.group, elev_m) %>% summarise_at(c("cwd", "pck", "ppt", "tmn", "tmx"), c(mean, sem), na.rm = TRUE)
names(allpops_recent_grwseason_avgs) <- gsub("fn2", "sem", colnames(allpops_recent_grwseason_avgs))
names(allpops_recent_grwseason_avgs) <-gsub("fn1", "mean", colnames(allpops_recent_grwseason_avgs))
allpops_recent_grwseason_avgs #30 year averages during growth season months ## # A tibble: 23 × 13
## # Groups: parent.pop, elevation.group [23]
## parent.pop elevation.group elev_m cwd_mean pck_mean ppt_mean tmn_mean
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 46.0 0 77.7 5.48
## 2 CC Low 313 41.0 0 122. 7.36
## 3 CP2 High 2244. 86.8 1.28 41.2 5.06
## 4 CP3 High 2266. 65.4 1.75 38.5 4.74
## 5 DPR Mid 1019. 28.8 0.869 102. 8.29
## 6 FR Mid 787 62.0 1.58 56.1 6.17
## 7 IH Low 454. 38.9 0.102 104. 7.48
## 8 LV1 High 2593. 71.7 2.06 49.0 3.05
## 9 LV3 High 2354. 88.3 2.73 51.3 2.92
## 10 LVTR1 High 2741. 81.4 2.03 50.5 2.74
## # ℹ 13 more rows
## # ℹ 6 more variables: tmx_mean <dbl>, cwd_sem <dbl>, pck_sem <dbl>,
## # ppt_sem <dbl>, tmn_sem <dbl>, tmx_sem <dbl>allpops_recent_grwseason_avgs$elevation.group <- factor(allpops_recent_grwseason_avgs$elevation.group, levels=elev_order)
allpops_recent_grwseason_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, cwd_mean), y=cwd_mean, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=cwd_mean-cwd_sem,ymax=cwd_mean+cwd_sem),width=.2, position =
position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0")+
labs(fill="Elevation",x="Population", y="Avg CWD" ,title = "Average CWD during Growth Season - Recent Climate") +
theme_classic() +
theme(text=element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/growthseasonAvg_CWD_RecentClim.png", width = 12, height = 6, units = "in")
allpops_recent_grwseason_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, ppt_mean), y=ppt_mean, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=ppt_mean-ppt_sem,ymax=ppt_mean+ppt_sem),width=.2, position =
position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation",x="Population", y="Avg Precip" ,title = "Average Precip during Growth Season - Recent Climate") +
theme_classic() +
theme(text=element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/growthseasonAvg_Precip_RecentClim.png", width = 12, height = 6, units = "in")
allpops_recent_grwseason_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, tmn_mean), y=tmn_mean, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=tmn_mean-tmn_sem,ymax=tmn_mean+tmn_sem),width=.2, position =
position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation",x="Population", y="Avg MinTemp" ,title = "Average MinTemp during Growth Season - Recent Climate") +
theme_classic() +
theme(text=element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/growthseasonAvg_MinTemp_RecentClim.png", width = 12, height = 6, units = "in")
allpops_recent_grwseason_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, tmx_mean), y=tmx_mean, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=tmx_mean-tmx_sem,ymax=tmx_mean+tmx_sem),width=.2, position =
position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation",x="Population", y="Avg MaxTemp" ,title = "Average MaxTemp during Growth Season - Recent Climate") +
theme_classic() +
theme(text=element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/growthseasonAvg_MaxTemp_RecentClim.png", width = 12, height = 6, units = "in")Historical Years - Growth Season
allpops_historical_grwseason_avgs <- allpops_historical_grwseason %>% group_by(parent.pop, elevation.group, elev_m) %>% summarise_at(c("cwd", "pck", "ppt", "tmn", "tmx"), c(mean, sem), na.rm = TRUE)
names(allpops_historical_grwseason_avgs) <- gsub("fn2", "sem", colnames(allpops_historical_grwseason_avgs))
names(allpops_historical_grwseason_avgs) <-gsub("fn1", "mean", colnames(allpops_historical_grwseason_avgs))
allpops_historical_grwseason_avgs #30 year averages during growth season months ## # A tibble: 23 × 13
## # Groups: parent.pop, elevation.group [23]
## parent.pop elevation.group elev_m cwd_mean pck_mean ppt_mean tmn_mean
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 511. 50.1 0.0294 71.5 4.73
## 2 CC Low 313 45.5 0.108 110. 6.65
## 3 CP2 High 2244. 84.7 1.57 44.0 3.98
## 4 CP3 High 2266. 62.3 1.86 42.3 3.64
## 5 DPR Mid 1019. 28.9 0.804 92.9 7.07
## 6 FR Mid 787 61.8 1.19 61.9 5.18
## 7 IH Low 454. 40.3 1.71 104. 6.51
## 8 LV1 High 2593. 67.9 2.47 64.0 0.823
## 9 LV3 High 2354. 85.3 2.69 66.4 0.705
## 10 LVTR1 High 2741. 78.0 2.49 67.3 0.600
## # ℹ 13 more rows
## # ℹ 6 more variables: tmx_mean <dbl>, cwd_sem <dbl>, pck_sem <dbl>,
## # ppt_sem <dbl>, tmn_sem <dbl>, tmx_sem <dbl>allpops_historical_grwseason_avgs$elevation.group <- factor(allpops_historical_grwseason_avgs$elevation.group, levels=elev_order)
allpops_historical_grwseason_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, cwd_mean), y=cwd_mean, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=cwd_mean-cwd_sem,ymax=cwd_mean+cwd_sem),width=.2, position =
position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0")+
labs(fill="Elevation",x="Population", y="Avg CWD" ,title = "Average CWD during Growth Season - Historical Climate") +
theme_classic() +
theme(text=element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/growthseasonAvg_CWD_HistoricalClim.png", width = 12, height = 6, units = "in")
allpops_historical_grwseason_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, ppt_mean), y=ppt_mean, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=ppt_mean-ppt_sem,ymax=ppt_mean+ppt_sem),width=.2, position =
position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation",x="Population", y="Avg Precip" ,title = "Average Precip during Growth Season - Historical Climate") +
theme_classic() +
theme(text=element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/growthseasonAvg_Precip_HistoricalClim.png", width = 12, height = 6, units = "in")
allpops_historical_grwseason_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, tmn_mean), y=tmn_mean, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=tmn_mean-tmn_sem,ymax=tmn_mean+tmn_sem),width=.2, position =
position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation",x="Population", y="Avg MinTemp" ,title = "Average MinTemp during Growth Season - Historical Climate") +
theme_classic() +
theme(text=element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/growthseasonAvg_MinTemp_HistoricalClim.png", width = 12, height = 6, units = "in")
allpops_historical_grwseason_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, tmx_mean), y=tmx_mean, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=tmx_mean-tmx_sem,ymax=tmx_mean+tmx_sem),width=.2, position =
position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation",x="Population", y="Avg MaxTemp" ,title = "Average MaxTemp during Growth Season - Historical Climate") +
theme_classic() +
theme(text=element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/growthseasonAvg_MaxTemp_HistoricalClim.png", width = 12, height = 6, units = "in")Recent Years - BioClim
names(bioclim_recent)## [1] "parent.pop" "elevation.group" "elev_m"
## [4] "year" "ann_tmean" "mean_diurnal_range"
## [7] "temp_seasonality" "temp_ann_range" "tmean_wettest_quarter"
## [10] "tmean_driest_quarter" "ann_ppt" "ppt_seasonality"
## [13] "ppt_warmest_quarter" "ppt_coldest_quarter"bioclim_recent_avgs <- bioclim_recent %>% group_by(parent.pop, elevation.group, elev_m) %>%
summarise_at(c("ann_tmean", "mean_diurnal_range", "temp_seasonality", "temp_ann_range", "tmean_wettest_quarter", "tmean_driest_quarter", "ann_ppt", "ppt_seasonality","ppt_warmest_quarter", "ppt_coldest_quarter"), c(mean, sem), na.rm = TRUE)
names(bioclim_recent_avgs) <- gsub("fn2", "sem", colnames(bioclim_recent_avgs))
names(bioclim_recent_avgs) <-gsub("fn1", "avg", colnames(bioclim_recent_avgs))
bioclim_recent_avgs## # A tibble: 23 × 23
## # Groups: parent.pop, elevation.group [23]
## parent.pop elevation.group elev_m ann_tmean_avg mean_diurnal_range_avg
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 BH Low 511. 16.2 14.7
## 2 CC Low 313 16.6 13.3
## 3 CP2 High 2244. 7.28 12.3
## 4 CP3 High 2266. 6.57 12.2
## 5 DPR Mid 1019. 14.0 12.4
## 6 FR Mid 787 12.6 14.6
## 7 IH Low 454. 15.4 13.6
## 8 LV1 High 2593. 4.89 12.7
## 9 LV3 High 2354. 4.87 12.6
## 10 LVTR1 High 2741. 4.74 12.8
## # ℹ 13 more rows
## # ℹ 18 more variables: temp_seasonality_avg <dbl>, temp_ann_range_avg <dbl>,
## # tmean_wettest_quarter_avg <dbl>, tmean_driest_quarter_avg <dbl>,
## # ann_ppt_avg <dbl>, ppt_seasonality_avg <dbl>,
## # ppt_warmest_quarter_avg <dbl>, ppt_coldest_quarter_avg <dbl>,
## # ann_tmean_sem <dbl>, mean_diurnal_range_sem <dbl>,
## # temp_seasonality_sem <dbl>, temp_ann_range_sem <dbl>, …write_csv(bioclim_recent_avgs, "../output/Climate/Pops_BioClimAvgs_Recent.csv")
bioclim_recent_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, ann_tmean_avg), y=ann_tmean_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=ann_tmean_avg-ann_tmean_sem,ymax=ann_tmean_avg+ann_tmean_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", y="Avg Annual Mean Temp", x="Population", title ="Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_AnnMeanTmp_RecentClim.png", width = 12, height = 6, units = "in")
bioclim_recent_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, mean_diurnal_range_avg), y=mean_diurnal_range_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=mean_diurnal_range_avg-mean_diurnal_range_sem,ymax=mean_diurnal_range_avg+mean_diurnal_range_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", y="Avg Diurnal Range", x="Population", title ="Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_DiurnalRange_RecentClim.png", width = 12, height = 6, units = "in")
bioclim_recent_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, temp_seasonality_avg), y=temp_seasonality_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=temp_seasonality_avg-temp_seasonality_sem,ymax=temp_seasonality_avg+temp_seasonality_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_TempSeasonality_RecentClim.png", width = 12, height = 6, units = "in")
bioclim_recent_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, temp_ann_range_avg), y=temp_ann_range_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=temp_ann_range_avg-temp_ann_range_sem,ymax=temp_ann_range_avg+temp_ann_range_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_AnnTmpRange_RecentClim.png", width = 12, height = 6, units = "in")
bioclim_recent_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, tmean_wettest_quarter_avg), y=tmean_wettest_quarter_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=tmean_wettest_quarter_avg-tmean_wettest_quarter_sem,ymax=tmean_wettest_quarter_avg+tmean_wettest_quarter_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_TmpMeanWet_RecentClim.png", width = 12, height = 6, units = "in")
bioclim_recent_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, tmean_driest_quarter_avg), y=tmean_driest_quarter_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=tmean_driest_quarter_avg-tmean_driest_quarter_sem,ymax=tmean_driest_quarter_avg+tmean_driest_quarter_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_TmpMeanDry_RecentClim.png", width = 12, height = 6, units = "in")
bioclim_recent_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, ann_ppt_avg), y=ann_ppt_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=ann_ppt_avg-ann_ppt_sem,ymax=ann_ppt_avg+ann_ppt_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_AnnPPT_RecentClim.png", width = 12, height = 6, units = "in")
bioclim_recent_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, ppt_seasonality_avg), y=ppt_seasonality_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=ppt_seasonality_avg-ppt_seasonality_sem,ymax=ppt_seasonality_avg+ppt_seasonality_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_PPTSeasonality_RecentClim.png", width = 12, height = 6, units = "in")
bioclim_recent_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, ppt_warmest_quarter_avg), y=ppt_warmest_quarter_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=ppt_warmest_quarter_avg-ppt_warmest_quarter_sem,ymax=ppt_warmest_quarter_avg+ppt_warmest_quarter_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_PPTWarm_RecentClim.png", width = 12, height = 6, units = "in")
bioclim_recent_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, ppt_coldest_quarter_avg), y=ppt_coldest_quarter_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=ppt_coldest_quarter_avg-ppt_coldest_quarter_sem,ymax=ppt_coldest_quarter_avg+ppt_coldest_quarter_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_PPTCold_RecentClim.png", width = 12, height = 6, units = "in")Historical Years - BioClim
names(bioclim_historical)## [1] "parent.pop" "elevation.group" "elev_m"
## [4] "year" "ann_tmean" "mean_diurnal_range"
## [7] "temp_seasonality" "temp_ann_range" "tmean_wettest_quarter"
## [10] "tmean_driest_quarter" "ann_ppt" "ppt_seasonality"
## [13] "ppt_warmest_quarter" "ppt_coldest_quarter"bioclim_historical_avgs <- bioclim_historical %>% group_by(parent.pop, elevation.group, elev_m) %>%
summarise_at(c("ann_tmean", "mean_diurnal_range", "temp_seasonality", "temp_ann_range", "tmean_wettest_quarter", "tmean_driest_quarter", "ann_ppt", "ppt_seasonality","ppt_warmest_quarter", "ppt_coldest_quarter"), c(mean, sem), na.rm = TRUE)
names(bioclim_historical_avgs) <- gsub("fn2", "sem", colnames(bioclim_historical_avgs))
names(bioclim_historical_avgs) <-gsub("fn1", "avg", colnames(bioclim_historical_avgs))
bioclim_historical_avgs## # A tibble: 23 × 23
## # Groups: parent.pop, elevation.group [23]
## parent.pop elevation.group elev_m ann_tmean_avg mean_diurnal_range_avg
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 BH Low 511. 15.3 15.3
## 2 CC Low 313 15.9 14.1
## 3 CP2 High 2244. 6.06 12.9
## 4 CP3 High 2266. 5.41 12.7
## 5 DPR Mid 1019. 12.9 13.5
## 6 FR Mid 787 12.0 15.8
## 7 IH Low 454. 14.7 14.2
## 8 LV1 High 2593. 3.38 13.8
## 9 LV3 High 2354. 3.35 13.7
## 10 LVTR1 High 2741. 3.23 13.8
## # ℹ 13 more rows
## # ℹ 18 more variables: temp_seasonality_avg <dbl>, temp_ann_range_avg <dbl>,
## # tmean_wettest_quarter_avg <dbl>, tmean_driest_quarter_avg <dbl>,
## # ann_ppt_avg <dbl>, ppt_seasonality_avg <dbl>,
## # ppt_warmest_quarter_avg <dbl>, ppt_coldest_quarter_avg <dbl>,
## # ann_tmean_sem <dbl>, mean_diurnal_range_sem <dbl>,
## # temp_seasonality_sem <dbl>, temp_ann_range_sem <dbl>, …write_csv(bioclim_historical_avgs, "../output/Climate/Pops_BioClimAvgs_Historical.csv")
bioclim_historical_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, ann_tmean_avg), y=ann_tmean_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=ann_tmean_avg-ann_tmean_sem,ymax=ann_tmean_avg+ann_tmean_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", y="Avg Annual Mean Temp", x="Population", title ="Historical Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_AnnMeanTmp_HistoricalClim.png", width = 12, height = 6, units = "in")
bioclim_historical_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, mean_diurnal_range_avg), y=mean_diurnal_range_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=mean_diurnal_range_avg-mean_diurnal_range_sem,ymax=mean_diurnal_range_avg+mean_diurnal_range_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", y="Avg Diurnal Range", x="Population", title ="Historical Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_DiurnalRange_HistoricalClim.png", width = 12, height = 6, units = "in")
bioclim_historical_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, temp_seasonality_avg), y=temp_seasonality_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=temp_seasonality_avg-temp_seasonality_sem,ymax=temp_seasonality_avg+temp_seasonality_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Historical Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_TempSeasonality_HistoricalClim.png", width = 12, height = 6, units = "in")
bioclim_historical_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, temp_ann_range_avg), y=temp_ann_range_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=temp_ann_range_avg-temp_ann_range_sem,ymax=temp_ann_range_avg+temp_ann_range_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Historical Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_AnnTmpRange_HistoricalClim.png", width = 12, height = 6, units = "in")
bioclim_historical_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, tmean_wettest_quarter_avg), y=tmean_wettest_quarter_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=tmean_wettest_quarter_avg-tmean_wettest_quarter_sem,ymax=tmean_wettest_quarter_avg+tmean_wettest_quarter_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Historical Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_TmpMeanWet_HistoricalClim.png", width = 12, height = 6, units = "in")
bioclim_historical_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, tmean_driest_quarter_avg), y=tmean_driest_quarter_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=tmean_driest_quarter_avg-tmean_driest_quarter_sem,ymax=tmean_driest_quarter_avg+tmean_driest_quarter_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Historical Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_TmpMeanDry_HistoricalClim.png", width = 12, height = 6, units = "in")
bioclim_historical_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, ann_ppt_avg), y=ann_ppt_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=ann_ppt_avg-ann_ppt_sem,ymax=ann_ppt_avg+ann_ppt_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Historical Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_AnnPPT_HistoricalClim.png", width = 12, height = 6, units = "in")
bioclim_historical_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, ppt_seasonality_avg), y=ppt_seasonality_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=ppt_seasonality_avg-ppt_seasonality_sem,ymax=ppt_seasonality_avg+ppt_seasonality_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Historical Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_PPTSeasonality_HistoricalClim.png", width = 12, height = 6, units = "in")
bioclim_historical_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, ppt_warmest_quarter_avg), y=ppt_warmest_quarter_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=ppt_warmest_quarter_avg-ppt_warmest_quarter_sem,ymax=ppt_warmest_quarter_avg+ppt_warmest_quarter_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Historical Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_PPTWarm_HistoricalClim.png", width = 12, height = 6, units = "in")
bioclim_historical_avgs %>% ggplot(aes(x=fct_reorder(parent.pop, ppt_coldest_quarter_avg), y=ppt_coldest_quarter_avg, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=ppt_coldest_quarter_avg-ppt_coldest_quarter_sem,ymax=ppt_coldest_quarter_avg+ppt_coldest_quarter_sem),width=.2, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)", x="Population", title ="Historical Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("../output/Climate/Bioclim_PPTCold_HistoricalClim.png", width = 12, height = 6, units = "in")Climate trait correlations
Flint
head(allpops_recent_grwseason)## # A tibble: 6 × 14
## # Groups: parent.pop, elevation.group, elev_m, year [6]
## month growmonth parent.pop elevation.group elev_m PckSum year cwd pck
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 6 BH Low 511. 0 1992 28.3 0
## 2 1 6 BH Low 511. 0 1993 25.6 0
## 3 1 6 BH Low 511. 0 1994 31.3 0
## 4 1 6 BH Low 511. 0 1995 27.9 0
## 5 1 6 BH Low 511. 0 1996 31.3 0
## 6 1 6 BH Low 511. 0 1997 26.1 0
## # ℹ 5 more variables: ppt <dbl>, tmn <dbl>, tmx <dbl>, firstmonth <dbl>,
## # lastmonth <dbl>allpops_recent_grwseason <- tibble(allpops_recent_grwseason)
allpops_recent_grwseason %>% cor_test(cwd, ppt, tmn, tmx, method = "pearson")## # A tibble: 16 × 8
## var1 var2 cor statistic p conf.low conf.high method
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 cwd cwd 1 Inf 0 1 1 Pearson
## 2 cwd ppt -0.54 -45.9 0 -0.555 -0.517 Pearson
## 3 cwd tmn 0.43 34.6 1.26e-236 0.410 0.454 Pearson
## 4 cwd tmx 0.55 47.0 0 0.526 0.565 Pearson
## 5 ppt cwd -0.54 -45.9 0 -0.555 -0.517 Pearson
## 6 ppt ppt 1 Inf 0 1 1 Pearson
## 7 ppt tmn -0.34 -25.9 2.55e-139 -0.362 -0.313 Pearson
## 8 ppt tmx -0.52 -43.4 0 -0.535 -0.495 Pearson
## 9 tmn cwd 0.43 34.6 1.26e-236 0.410 0.454 Pearson
## 10 tmn ppt -0.34 -25.9 2.55e-139 -0.362 -0.313 Pearson
## 11 tmn tmn 1 Inf 0 1 1 Pearson
## 12 tmn tmx 0.94 193. 0 0.933 0.940 Pearson
## 13 tmx cwd 0.55 47.0 0 0.526 0.565 Pearson
## 14 tmx ppt -0.52 -43.4 0 -0.535 -0.495 Pearson
## 15 tmx tmn 0.94 193. 0 0.933 0.940 Pearson
## 16 tmx tmx 1 Inf 0 1 1 Pearsonrecent_cor_mat <- allpops_recent_grwseason %>% select(cwd, ppt, tmn, tmx) %>% cor_mat()
recent_cor_mat## # A tibble: 4 × 5
## rowname cwd ppt tmn tmx
## * <chr> <dbl> <dbl> <dbl> <dbl>
## 1 cwd 1 -0.54 0.43 0.55
## 2 ppt -0.54 1 -0.34 -0.52
## 3 tmn 0.43 -0.34 1 0.94
## 4 tmx 0.55 -0.52 0.94 1recent_cor = allpops_recent_grwseason %>% select(cwd, ppt, tmn, tmx) %>% cor()
file_path= "../output/Climate/GrowthSeason_RecentClim_Cors.png"
png(width = 12, height = 6, res= 300, units = "in", file=file_path, type = "cairo")
corrplot(recent_cor)
dev.off()## quartz_off_screen
## 2allpops_historical_grwseason <- tibble(allpops_historical_grwseason)
allpops_historical_grwseason %>% cor_test(cwd, ppt, tmn, tmx, method = "pearson")## # A tibble: 16 × 8
## var1 var2 cor statistic p conf.low conf.high method
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 cwd cwd 1 Inf 0 1 1 Pearson
## 2 cwd ppt -0.51 -41.5 1.98e-323 -0.526 -0.485 Pearson
## 3 cwd tmn 0.39 29.8 2.32e-179 0.364 0.411 Pearson
## 4 cwd tmx 0.53 43.8 0 0.506 0.546 Pearson
## 5 ppt cwd -0.51 -41.5 1.98e-323 -0.526 -0.485 Pearson
## 6 ppt ppt 1 3358796967. 0 1 1 Pearson
## 7 ppt tmn -0.31 -23.3 3.49e-114 -0.338 -0.288 Pearson
## 8 ppt tmx -0.5 -41.1 7.47e-319 -0.523 -0.481 Pearson
## 9 tmn cwd 0.39 29.8 2.32e-179 0.364 0.411 Pearson
## 10 tmn ppt -0.31 -23.3 3.49e-114 -0.338 -0.288 Pearson
## 11 tmn tmn 1 Inf 0 1 1 Pearson
## 12 tmn tmx 0.92 166. 0 0.915 0.924 Pearson
## 13 tmx cwd 0.53 43.8 0 0.506 0.546 Pearson
## 14 tmx ppt -0.5 -41.1 7.47e-319 -0.523 -0.481 Pearson
## 15 tmx tmn 0.92 166. 0 0.915 0.924 Pearson
## 16 tmx tmx 1 Inf 0 1 1 Pearsonhistorical_cor_mat <- allpops_historical_grwseason %>% select(cwd, ppt, tmn, tmx) %>% cor_mat()
historical_cor_mat## # A tibble: 4 × 5
## rowname cwd ppt tmn tmx
## * <chr> <dbl> <dbl> <dbl> <dbl>
## 1 cwd 1 -0.51 0.39 0.53
## 2 ppt -0.51 1 -0.31 -0.5
## 3 tmn 0.39 -0.31 1 0.92
## 4 tmx 0.53 -0.5 0.92 1historical_cor = allpops_historical_grwseason %>% select(cwd, ppt, tmn, tmx) %>% cor()
file_path= "../output/Climate/GrowthSeason_HistoricalClim_Cors.png"
png(width = 12, height = 6, res= 300, units = "in", file=file_path, type = "cairo")
corrplot(historical_cor)
dev.off()## quartz_off_screen
## 2Trait correlations are the same across recent and historical time periods (during the growth season)
Bioclim
names(bioclim_recent)## [1] "parent.pop" "elevation.group" "elev_m"
## [4] "year" "ann_tmean" "mean_diurnal_range"
## [7] "temp_seasonality" "temp_ann_range" "tmean_wettest_quarter"
## [10] "tmean_driest_quarter" "ann_ppt" "ppt_seasonality"
## [13] "ppt_warmest_quarter" "ppt_coldest_quarter"bioclim_recent <- tibble(bioclim_recent)
bioclim_recent %>% cor_test(ann_tmean:ppt_coldest_quarter, method = "pearson")## # A tibble: 100 × 8
## var1 var2 cor statistic p conf.low conf.high method
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 ann_tmean ann_tmean 1 Inf 0 1 1 Pears…
## 2 ann_tmean mean_diurnal_… 0.34 9.49 3.77e- 20 0.272 0.405 Pears…
## 3 ann_tmean temp_seasonal… 0.064 1.69 9.23e- 2 -0.0106 0.138 Pears…
## 4 ann_tmean temp_ann_range 0.11 2.92 3.62e- 3 0.0363 0.184 Pears…
## 5 ann_tmean tmean_wettest… 0.94 71.6 3.06e-321 0.930 0.947 Pears…
## 6 ann_tmean tmean_driest_… 0.95 83.0 0 0.946 0.960 Pears…
## 7 ann_tmean ann_ppt -0.31 -8.70 2.39e- 17 -0.381 -0.246 Pears…
## 8 ann_tmean ppt_seasonali… 0.2 5.33 1.34e- 7 0.126 0.270 Pears…
## 9 ann_tmean ppt_warmest_q… -0.48 -14.5 7.98e- 42 -0.539 -0.425 Pears…
## 10 ann_tmean ppt_coldest_q… -0.24 -6.60 8.11e- 11 -0.313 -0.173 Pears…
## # ℹ 90 more rowsrecent_cor_bioclim_mat <- bioclim_recent %>% select(ann_tmean:ppt_coldest_quarter) %>% cor_mat()
recent_cor_bioclim_mat## # A tibble: 10 × 11
## rowname ann_tmean mean_diurnal_range temp_seasonality temp_ann_range
## * <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ann_tmean 1 0.34 0.064 0.11
## 2 mean_diurnal_ra… 0.34 1 0.061 0.5
## 3 temp_seasonality 0.064 0.061 1 0.66
## 4 temp_ann_range 0.11 0.5 0.66 1
## 5 tmean_wettest_q… 0.94 0.28 -0.019 0.026
## 6 tmean_driest_qu… 0.95 0.31 0.16 0.17
## 7 ann_ppt -0.31 -0.46 -0.14 -0.27
## 8 ppt_seasonality 0.2 0.074 -0.037 -0.064
## 9 ppt_warmest_qua… -0.48 -0.19 -0.16 -0.066
## 10 ppt_coldest_qua… -0.24 -0.39 -0.17 -0.26
## # ℹ 6 more variables: tmean_wettest_quarter <dbl>, tmean_driest_quarter <dbl>,
## # ann_ppt <dbl>, ppt_seasonality <dbl>, ppt_warmest_quarter <dbl>,
## # ppt_coldest_quarter <dbl>recent_cor_bioclim = bioclim_recent %>% select(ann_tmean:ppt_coldest_quarter) %>% cor()
file_path= "../output/Climate/BioClim_RecentClim_Cors.png"
png(width = 12, height = 6, res= 300, units = "in", file=file_path, type = "cairo")
corrplot(recent_cor_bioclim)
dev.off()## quartz_off_screen
## 2bioclim_historical <- tibble(bioclim_historical)
bioclim_historical %>% cor_test(ann_tmean:ppt_coldest_quarter, method = "pearson")## # A tibble: 100 × 8
## var1 var2 cor statistic p conf.low conf.high method
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 ann_tmean ann_tmean 1 Inf 0 1 1 Pears…
## 2 ann_tmean mean_diurnal_ra… 0.37 10.3 2.53e-23 0.300 0.429 Pears…
## 3 ann_tmean temp_seasonality 0.2 5.35 1.17e- 7 0.127 0.271 Pears…
## 4 ann_tmean temp_ann_range 0.23 6.20 9.89e-10 0.158 0.299 Pears…
## 5 ann_tmean tmean_wettest_q… 0.96 85.8 0 0.949 0.962 Pears…
## 6 ann_tmean tmean_driest_qu… 0.95 82.2 0 0.945 0.959 Pears…
## 7 ann_tmean ann_ppt -0.36 -10.2 1.15e-22 -0.424 -0.294 Pears…
## 8 ann_tmean ppt_seasonality 0.2 5.30 1.56e- 7 0.125 0.269 Pears…
## 9 ann_tmean ppt_warmest_qua… -0.48 -14.4 4.49e-41 -0.536 -0.421 Pears…
## 10 ann_tmean ppt_coldest_qua… -0.19 -5.21 2.55e- 7 -0.265 -0.122 Pears…
## # ℹ 90 more rowshistorical_cor_bioclim_mat <- bioclim_historical %>% select(ann_tmean:ppt_coldest_quarter) %>% cor_mat()
historical_cor_bioclim_mat## # A tibble: 10 × 11
## rowname ann_tmean mean_diurnal_range temp_seasonality temp_ann_range
## * <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ann_tmean 1 0.37 0.2 0.23
## 2 mean_diurnal_ra… 0.37 1 0.22 0.69
## 3 temp_seasonality 0.2 0.22 1 0.65
## 4 temp_ann_range 0.23 0.69 0.65 1
## 5 tmean_wettest_q… 0.96 0.33 0.072 0.15
## 6 tmean_driest_qu… 0.95 0.35 0.33 0.32
## 7 ann_ppt -0.36 -0.4 -0.14 -0.31
## 8 ppt_seasonality 0.2 0.077 -0.027 0.047
## 9 ppt_warmest_qua… -0.48 -0.17 -0.31 -0.21
## 10 ppt_coldest_qua… -0.19 -0.35 -0.13 -0.29
## # ℹ 6 more variables: tmean_wettest_quarter <dbl>, tmean_driest_quarter <dbl>,
## # ann_ppt <dbl>, ppt_seasonality <dbl>, ppt_warmest_quarter <dbl>,
## # ppt_coldest_quarter <dbl>historical_cor_bioclim = bioclim_historical %>% select(ann_tmean:ppt_coldest_quarter) %>% cor()
file_path= "../output/Climate/BioClim_HistoricalClim_Cors.png"
png(width = 12, height = 6, res= 300, units = "in", file=file_path, type = "cairo")
corrplot(historical_cor_bioclim)
dev.off()## quartz_off_screen
## 2Correlations vary slightly between recent and historical climate
Climate PCAs
- Should use this code to check the significance of the PCA: https://github.com/StatQuest/pca_demo/blob/master/pca_demo.R
- Remember this paper: Björklund, M. 2019. Be careful with your principal components. Evolution 73: 2151–2158.
All years and months included (Flint)
Produce basic PCA plots - Flint Recent
#normalize the data
climate_normalized <- allpops_recent_grwseason %>% select(cwd, ppt, tmn, tmx) %>% scale() #normalize the data so they're all on the same scale
head(climate_normalized)## cwd ppt tmn tmx
## [1,] -0.9340134 -0.2497441 -0.9013383 -1.2858827
## [2,] -1.0118972 2.5278439 -0.7797138 -1.0556326
## [3,] -0.8471874 -0.2305712 -0.6844413 -0.6258327
## [4,] -0.9461286 2.4029944 -0.1128060 -0.9297627
## [5,] -0.8463220 0.1592023 -0.4553818 -0.7808676
## [6,] -0.9957436 2.9150233 -0.3661905 -0.9696727cor.norm = cor(climate_normalized) #test correlations among the traits
corrplot(cor.norm)
climate.pca <- princomp(cor.norm)
summary(climate.pca) #PC1 explains 91% of the variation, PC2 explains 9.3%, together they explain 99%## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4
## Standard deviation 1.119571 0.3477514 0.0358444403 0
## Proportion of Variance 0.911158 0.0879080 0.0009339726 0
## Cumulative Proportion 0.911158 0.9990660 1.0000000000 1climate.pca$loadings[, 1:2] #PC1 has high positive loading values for tmn, tmx, cwd## Comp.1 Comp.2
## cwd 0.4512467 0.6904085
## ppt -0.5644029 -0.2516209
## tmn 0.4458240 -0.5544344
## tmx 0.5282676 -0.3906732fviz_eig(climate.pca) #scree plot 
fviz_pca_var(climate.pca, col.var = "black") #biplot
#The goal of the third visualization is to determine how much each variable is represented in a given component. Such a quality of representation is called the Cos2 and corresponds to the square cosine, and it is computed using the fviz_cos2 function. (https://www.datacamp.com/tutorial/pca-analysis-r)
#A low value means that the variable is not perfectly represented by that component.
#A high value, on the other hand, means a good representation of the variable on that component.
fviz_cos2(climate.pca, choice = "var", axes = 1:2) #axes says which PCs to use 
#biplot combined wiht cos2
file_path= "../output/Climate/RecentClim_GrowthSeason_PCA.png"
png(width = 5.5, height = 5, res= 300, units = "in", file=file_path, type = "cairo")
fviz_pca_var(climate.pca, col.var = "cos2",
gradient.cols = c("black", "orange", "green"),
repel = TRUE)
dev.off()## quartz_off_screen
## 2Overlay populations on PCA plot - Flint Recent
pc = prcomp(climate_normalized, scale = TRUE)
pc_data = data.frame(pc$x)
locs_pc = cbind(allpops_recent_grwseason, pc_data)
loadings = data.frame(varnames=rownames(pc$rotation), pc$rotation)
ggplot() +
geom_point(data = filter(locs_pc, elevation.group == "High"), aes(x = PC1, y = PC2), color = "#0043F0", alpha = 0.6) +
geom_point(data = filter(locs_pc, elevation.group == "Mid"), aes(x = PC1, y = PC2), color = "#C9727F", alpha = 0.6) +
geom_point(data = filter(locs_pc, elevation.group == "Low"), aes(x = PC1, y = PC2), color = "#F5A540", alpha = 0.6) +
geom_vline(xintercept = 0, linetype="dashed") + geom_hline(yintercept = 0, linetype="dashed") +
theme_classic()
ggsave("../output/Climate/RecentClim_PCA_Elev.png", width = 6, height = 4, units = "in")Average across all years and months (1 datapoint per pop) - Flint
Produce basic PCA plots - Flint Recent
#normalize the data
climate_normalized_avgs <- allpops_recent_grwseason_avgs %>% ungroup() %>% select(cwd_mean, ppt_mean, tmn_mean, tmx_mean) %>% scale() #normalize the data so they're all on the same scale
head(climate_normalized_avgs)## cwd_mean ppt_mean tmn_mean tmx_mean
## [1,] -0.87171192 0.4929802 0.06812917 -0.1741032
## [2,] -1.12918772 1.8564402 1.10022273 0.2638439
## [3,] 1.20082422 -0.6288554 -0.16087245 -0.2227265
## [4,] 0.11203342 -0.7125966 -0.33691807 -0.5920547
## [5,] -1.74834788 1.2370860 1.61198995 1.4853656
## [6,] -0.06227222 -0.1720920 0.44669436 1.9126197cor.norm_avgs = cor(climate_normalized_avgs) #test correlations among the traits
file_path= "../output/Climate/GrowthSeason_RecentClim_Cors_Avgs.png"
png(width = 12, height = 6, res= 300, units = "in", file=file_path, type = "cairo")
corrplot(cor.norm_avgs)
dev.off()## quartz_off_screen
## 2climate.pca_avgs <- princomp(cor.norm_avgs)
summary(climate.pca_avgs) #PC1 explains 90% of the variation, PC2 explains 8.1%, together they explain 98%## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4
## Standard deviation 1.3530363 0.28915484 0.074703339 0
## Proportion of Variance 0.9535438 0.04354945 0.002906711 0
## Cumulative Proportion 0.9535438 0.99709329 1.000000000 1climate.pca_avgs$loadings[, 1:2] #PC1 has high positive loading values for tmn, tmx, cwd## Comp.1 Comp.2
## cwd_mean 0.5557510 0.2711216
## ppt_mean -0.5163709 -0.5524523
## tmn_mean -0.5073302 0.2595903
## tmx_mean -0.4088007 0.7442462fviz_eig(climate.pca_avgs) #scree plot 
fviz_pca_var(climate.pca_avgs, col.var = "black") #biplot
#The goal of the third visualization is to determine how much each variable is represented in a given component. Such a quality of representation is called the Cos2 and corresponds to the square cosine, and it is computed using the fviz_cos2 function. (https://www.datacamp.com/tutorial/pca-analysis-r)
#A low value means that the variable is not perfectly represented by that component.
#A high value, on the other hand, means a good representation of the variable on that component.
fviz_cos2(climate.pca_avgs, choice = "var", axes = 1:2) #axes says which PCs to use 
#biplot combined wiht cos2
file_path= "../output/Climate/RecentClim_GrowthSeason_PCA_Avgs.png"
png(width = 5.5, height = 5, res= 300, units = "in", file=file_path, type = "cairo")
fviz_pca_var(climate.pca_avgs, col.var = "cos2",
gradient.cols = c("black", "orange", "green"),
repel = TRUE)
dev.off()## quartz_off_screen
## 2Overlay populations on PCA plot - Flint Recent
pc_avgs = prcomp(climate_normalized_avgs, scale = TRUE)
pc_data = data.frame(pc_avgs$x)
locs_pc = cbind(allpops_recent_grwseason_avgs, pc_data)
names(locs_pc)## [1] "parent.pop" "elevation.group" "elev_m" "cwd_mean"
## [5] "pck_mean" "ppt_mean" "tmn_mean" "tmx_mean"
## [9] "cwd_sem" "pck_sem" "ppt_sem" "tmn_sem"
## [13] "tmx_sem" "PC1" "PC2" "PC3"
## [17] "PC4"loadings = data.frame(varnames=rownames(pc_avgs$rotation), pc_avgs$rotation)
ggplot() +
geom_point(data = filter(locs_pc, elevation.group == "High"), size=2, aes(x = PC1, y = PC2), color = "#0043F0") +
geom_point(data = filter(locs_pc, elevation.group == "Mid"), size=2, aes(x = PC1, y = PC2), color = "#C9727F") +
geom_point(data = filter(locs_pc, elevation.group == "Low"), size=2, aes(x = PC1, y = PC2), color = "#F5A540") +
geom_label_repel(data = locs_pc, aes(x = PC1, y = PC2, label = parent.pop),
min.segment.length = 0, box.padding = 0.5) +
geom_vline(xintercept = 0, linetype="dashed") + geom_hline(yintercept = 0, linetype="dashed") +
theme_classic()
ggsave("../output/Climate/RecentClim_PCA_Elev_Avgs.png", width = 6, height = 4, units = "in")Average across years per month (Flint)
Calculate monthly averages - Flint Recent
names(allpops_recent_grwseason)## [1] "month" "growmonth" "parent.pop" "elevation.group"
## [5] "elev_m" "PckSum" "year" "cwd"
## [9] "pck" "ppt" "tmn" "tmx"
## [13] "firstmonth" "lastmonth"allpops_recent_grwseason_mosavgs <- allpops_recent_grwseason %>% group_by(parent.pop, elevation.group, month) %>% summarise_at(c("cwd", "ppt", "tmn", "tmx"), c(mean, sem), na.rm = TRUE)
names(allpops_recent_grwseason_mosavgs) <- gsub("fn2", "sem", colnames(allpops_recent_grwseason_mosavgs))
names(allpops_recent_grwseason_mosavgs) <-gsub("fn1", "mean", colnames(allpops_recent_grwseason_mosavgs))
allpops_recent_grwseason_mosavgs #30 year averages per growth season month ## # A tibble: 247 × 11
## # Groups: parent.pop, elevation.group [23]
## parent.pop elevation.group month cwd_mean ppt_mean tmn_mean tmx_mean cwd_sem
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BH Low 1 29.3 129. 2.76 13.8 0.444
## 2 BH Low 2 40.9 103. 3.51 15.0 0.509
## 3 BH Low 3 53.6 87.5 4.88 17.4 1.56
## 4 BH Low 4 58.5 48.3 6.34 20.4 2.97
## 5 BH Low 5 50.6 23.4 9.74 25.8 5.49
## 6 BH Low 6 41.8 24.3 12.6 29.6 5.34
## 7 BH Low 7 80.9 1.42 15.6 33.5 NA
## 8 BH Low 10 87.6 74.0 9.29 24.1 0.580
## 9 BH Low 11 45.0 56.9 5.04 18.0 0.548
## 10 BH Low 12 29.8 112. 2.43 13.5 0.347
## # ℹ 237 more rows
## # ℹ 3 more variables: ppt_sem <dbl>, tmn_sem <dbl>, tmx_sem <dbl>allpops_recent_grwseason_mosavgs$elevation.group <- factor(allpops_recent_grwseason_mosavgs$elevation.group, levels=elev_order) Produce basic PCA plots - Flint Recent
Color by growth month relative to growth season?
names(allpops_recent_grwseason_mosavgs)## [1] "parent.pop" "elevation.group" "month" "cwd_mean"
## [5] "ppt_mean" "tmn_mean" "tmx_mean" "cwd_sem"
## [9] "ppt_sem" "tmn_sem" "tmx_sem"#normalize the data
climate_normalized_mosavgs <- allpops_recent_grwseason_mosavgs %>% ungroup() %>% select(cwd_mean, ppt_mean, tmn_mean, tmx_mean) %>% scale() #normalize the data so they're all on the same scale
head(climate_normalized_mosavgs)## cwd_mean ppt_mean tmn_mean tmx_mean
## [1,] -0.86381036 1.1730807 -0.32392500 -0.56519207
## [2,] -0.49640236 0.7089039 -0.19276000 -0.40499983
## [3,] -0.09590962 0.4242495 0.04622095 -0.06152499
## [4,] 0.05922915 -0.2850169 0.30324161 0.35510929
## [5,] -0.18894478 -0.7357748 0.89782812 1.10724955
## [6,] -0.46975807 -0.7187571 1.39146917 1.63951553cor.norm_mosavgs = cor(climate_normalized_mosavgs) #test correlations among the traits
file_path= "../output/Climate/GrowthSeason_RecentClim_Cors_MonthlyAvgs.png"
png(width = 12, height = 6, res= 300, units = "in", file=file_path, type = "cairo")
corrplot(cor.norm_mosavgs)
dev.off()## quartz_off_screen
## 2climate.pca_mosavgs <- princomp(cor.norm_mosavgs)
summary(climate.pca_mosavgs) #PC1 explains 90% of the variation, PC2 explains 8.1%, together they explain 98%## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4
## Standard deviation 1.1931646 0.32381553 0.0237242226 0
## Proportion of Variance 0.9310562 0.06857574 0.0003680943 0
## Cumulative Proportion 0.9310562 0.99963191 1.0000000000 1climate.pca_mosavgs$loadings[, 1:2] #PC1 has high positive loading values for tmn, tmx, cwd## Comp.1 Comp.2
## cwd_mean 0.4789655 0.6443253
## ppt_mean -0.5535691 -0.2958083
## tmn_mean 0.4504181 -0.5551376
## tmx_mean 0.5111524 -0.4349307fviz_eig(climate.pca_mosavgs) #scree plot 
fviz_pca_var(climate.pca_mosavgs, col.var = "black") #biplot
#The goal of the third visualization is to determine how much each variable is represented in a given component. Such a quality of representation is called the Cos2 and corresponds to the square cosine, and it is computed using the fviz_cos2 function. (https://www.datacamp.com/tutorial/pca-analysis-r)
#A low value means that the variable is not perfectly represented by that component.
#A high value, on the other hand, means a good representation of the variable on that component.
fviz_cos2(climate.pca_mosavgs, choice = "var", axes = 1:2) #axes says which PCs to use 
#biplot combined wiht cos2
file_path= "../output/Climate/RecentClim_GrowthSeason_PCA_MonthlyAvgs.png"
png(width = 5.5, height = 5, res= 300, units = "in", file=file_path, type = "cairo")
fviz_pca_var(climate.pca_mosavgs, col.var = "cos2",
gradient.cols = c("black", "orange", "green"),
repel = TRUE)
dev.off()## quartz_off_screen
## 2Overlay populations on PCA plot - Flint Recent
pc_mosavgs = prcomp(climate_normalized_mosavgs, scale = TRUE)
pc_data = data.frame(pc_mosavgs$x)
locs_pc = cbind(allpops_recent_grwseason_mosavgs, pc_data)
loadings = data.frame(varnames=rownames(pc_mosavgs$rotation), pc_mosavgs$rotation)
ggplot() +
geom_point(data = filter(locs_pc, elevation.group == "High"), size=2, aes(x = PC1, y = PC2), color = "#0043F0") +
geom_point(data = filter(locs_pc, elevation.group == "Mid"), size=2, aes(x = PC1, y = PC2), color = "#C9727F") +
geom_point(data = filter(locs_pc, elevation.group == "Low"), size=2, aes(x = PC1, y = PC2), color = "#F5A540") +
geom_text_repel(data = locs_pc, aes(x = PC1, y = PC2, label = parent.pop),
min.segment.length = 0) +
geom_vline(xintercept = 0, linetype="dashed") + geom_hline(yintercept = 0, linetype="dashed") +
theme_classic()## Warning: ggrepel: 72 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ggsave("../output/Climate/RecentClim_PCA_Elev_MonthlyAvgs_Pops.png", width = 6, height = 4, units = "in")## Warning: ggrepel: 132 unlabeled data points (too many overlaps). Consider
## increasing max.overlapsggplot() +
geom_point(data = filter(locs_pc, elevation.group == "High"), size=2, aes(x = PC1, y = PC2), color = "#0043F0") +
geom_point(data = filter(locs_pc, elevation.group == "Mid"), size=2, aes(x = PC1, y = PC2), color = "#C9727F") +
geom_point(data = filter(locs_pc, elevation.group == "Low"), size=2, aes(x = PC1, y = PC2), color = "#F5A540") +
geom_text_repel(data = locs_pc, aes(x = PC1, y = PC2, label = month),
min.segment.length = 0) +
geom_vline(xintercept = 0, linetype="dashed") + geom_hline(yintercept = 0, linetype="dashed") +
theme_classic()## Warning: ggrepel: 16 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ggsave("../output/Climate/RecentClim_PCA_Elev_MonthlyAvgs_Months.png", width = 6, height = 4, units = "in")## Warning: ggrepel: 54 unlabeled data points (too many overlaps). Consider
## increasing max.overlapsAll years included (BioClim)
Produce basic PCA plots - BioClim Recent
#normalize the data
climate_normalized <- bioclim_recent %>% select(ann_tmean:ppt_coldest_quarter) %>% scale() #normalize the data so they're all on the same scale
head(climate_normalized)## ann_tmean mean_diurnal_range temp_seasonality temp_ann_range
## [1,] 1.488048 1.484196 0.6345835 1.51641105
## [2,] 1.292581 1.793357 -0.9850409 -0.27252427
## [3,] 1.309042 2.048352 1.1007088 1.28430291
## [4,] 1.423970 1.299199 -1.7766975 -0.69711355
## [5,] 1.493926 1.734191 -0.2298294 0.57665287
## [6,] 1.495396 1.786690 -0.5789875 0.02185609
## tmean_wettest_quarter tmean_driest_quarter ann_ppt ppt_seasonality
## [1,] 0.9477349 0.3062418 -1.2499359 0.6552141
## [2,] 1.3701910 1.1218507 -1.0038190 1.3630755
## [3,] 0.9804684 1.4338282 -1.4555166 -1.3703942
## [4,] 1.5323185 0.8549045 -0.6963120 1.0690546
## [5,] 1.4110117 1.4806609 -0.4258456 0.1266306
## [6,] 1.3397678 1.3117031 -1.4051095 5.1279468
## ppt_warmest_quarter ppt_coldest_quarter
## [1,] 0.3981273 -0.8725818
## [2,] -0.9374355 -0.5158821
## [3,] -0.7864416 -1.4995947
## [4,] -0.8716482 -0.6832578
## [5,] -0.5811534 -0.2660452
## [6,] -0.8526253 -0.8530541cor.norm = cor(climate_normalized) #test correlations among the traits
corrplot(cor.norm)
climate.pca <- princomp(cor.norm)
summary(climate.pca) #PC1 explains 91% of the variation, PC2 explains 9.3%, together they explain 99%## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 1.150707 0.6298924 0.36071490 0.32191161 0.22017834
## Proportion of Variance 0.658955 0.1974509 0.06475219 0.05157029 0.02412545
## Cumulative Proportion 0.658955 0.8564059 0.92115807 0.97272835 0.99685381
## Comp.6 Comp.7 Comp.8 Comp.9
## Standard deviation 0.069756668 0.0323147710 0.0174575349 1.034719e-02
## Proportion of Variance 0.002421574 0.0005196709 0.0001516674 5.328086e-05
## Cumulative Proportion 0.999275381 0.9997950518 0.9999467191 1.000000e+00
## Comp.10
## Standard deviation 1.139135e-08
## Proportion of Variance 6.457681e-17
## Cumulative Proportion 1.000000e+00climate.pca$loadings[, 1:2] #PC1 has high positive loading values for tmn, tmx, cwd## Comp.1 Comp.2
## ann_tmean 0.42218724 0.2844874
## mean_diurnal_range 0.28501158 -0.2600425
## temp_seasonality 0.10189008 -0.4042994
## temp_ann_range 0.15955805 -0.5159323
## tmean_wettest_quarter 0.41461687 0.3010946
## tmean_driest_quarter 0.41265005 0.2590390
## ann_ppt -0.37993290 0.2892133
## ppt_seasonality 0.04697644 0.1821506
## ppt_warmest_quarter -0.30075753 -0.2056759
## ppt_coldest_quarter -0.35424269 0.3239069fviz_eig(climate.pca) #scree plot 
fviz_pca_var(climate.pca, col.var = "black") #biplot
#The goal of the third visualization is to determine how much each variable is represented in a given component. Such a quality of representation is called the Cos2 and corresponds to the square cosine, and it is computed using the fviz_cos2 function. (https://www.datacamp.com/tutorial/pca-analysis-r)
#A low value means that the variable is not perfectly represented by that component.
#A high value, on the other hand, means a good representation of the variable on that component.
fviz_cos2(climate.pca, choice = "var", axes = 1:2) #axes says which PCs to use 
#biplot combined wiht cos2
file_path= "../output/Climate/RecentClim_BioClim_PCA.png"
png(width = 5.5, height = 5, res= 300, units = "in", file=file_path, type = "cairo")
fviz_pca_var(climate.pca, col.var = "cos2",
gradient.cols = c("black", "orange", "green"),
repel = TRUE)
dev.off()## quartz_off_screen
## 2Overlay populations on PCA plot - BioClim Recent
pc = prcomp(climate_normalized, scale = TRUE)
pc_data = data.frame(pc$x)
locs_pc = cbind(bioclim_recent, pc_data)
loadings = data.frame(varnames=rownames(pc$rotation), pc$rotation)
ggplot() +
geom_point(data = filter(locs_pc, elevation.group == "High"), aes(x = PC1, y = PC2), color = "#0043F0", alpha = 0.6) +
geom_point(data = filter(locs_pc, elevation.group == "Mid"), aes(x = PC1, y = PC2), color = "#C9727F", alpha = 0.6) +
geom_point(data = filter(locs_pc, elevation.group == "Low"), aes(x = PC1, y = PC2), color = "#F5A540", alpha = 0.6) +
geom_vline(xintercept = 0, linetype="dashed") + geom_hline(yintercept = 0, linetype="dashed") +
theme_classic()
ggsave("../output/Climate/RecentClim_BioClimPCA_Elev.png", width = 6, height = 4, units = "in")Average across all years (1 datapoint per pop) - BioClim
Produce basic PCA plots - Bioclim Recent
#normalize the data
climate_normalized_avgs <- bioclim_recent_avgs %>% ungroup() %>% select(ann_tmean_avg:ppt_coldest_quarter_avg) %>% scale() #normalize the data so they're all on the same scale
head(climate_normalized_avgs)## ann_tmean_avg mean_diurnal_range_avg temp_seasonality_avg
## [1,] 1.4365744 2.1103241 0.7807743
## [2,] 1.5373314 0.3677399 -0.8002917
## [3,] -0.6392846 -0.7794069 0.3106801
## [4,] -0.8035757 -0.9284072 0.4993965
## [5,] 0.9327698 -0.6542612 0.5574192
## [6,] 0.6102852 1.9623190 2.0068328
## temp_ann_range_avg tmean_wettest_quarter_avg tmean_driest_quarter_avg
## [1,] 1.8936564 1.4365670 1.4092314
## [2,] -0.3106149 1.5348149 1.4696802
## [3,] -0.5013491 -0.6077896 -0.5261052
## [4,] -0.4954106 -0.7839883 -0.6694831
## [5,] -0.6071962 0.9485100 0.9987417
## [6,] 2.6723127 0.4768846 0.7235035
## ann_ppt_avg ppt_seasonality_avg ppt_warmest_quarter_avg
## [1,] -1.86140109 1.7404586 -1.3773151
## [2,] -0.49161686 0.7630087 -1.0853401
## [3,] 0.26032562 -0.8113405 0.5514082
## [4,] 0.09597021 -1.0345910 0.6482123
## [5,] 0.85590061 0.5143695 -0.7943749
## [6,] -0.58929595 0.4725516 -0.6697992
## ppt_coldest_quarter_avg
## [1,] -2.00643695
## [2,] -0.51718604
## [3,] 0.28983231
## [4,] 0.05138236
## [5,] 1.19209454
## [6,] -0.65810989cor.norm_avgs = cor(climate_normalized_avgs) #test correlations among the traits
file_path= "../output/Climate/BioClim_RecentClim_Cors_Avgs.png"
png(width = 12, height = 6, res= 300, units = "in", file=file_path, type = "cairo")
corrplot(cor.norm_avgs)
dev.off()## quartz_off_screen
## 2climate.pca_avgs <- princomp(cor.norm_avgs)
summary(climate.pca_avgs) #PC1 explains 90% of the variation, PC2 explains 8.1%, together they explain 98%## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 1.677552 0.6397661 0.28391871 0.24532861 0.076839605
## Proportion of Variance 0.835020 0.1214471 0.02391844 0.01785834 0.001751923
## Cumulative Proportion 0.835020 0.9564671 0.98038557 0.99824391 0.999995834
## Comp.6 Comp.7 Comp.8 Comp.9
## Standard deviation 3.687709e-03 4.832704e-04 3.726029e-04 2.634748e-04
## Proportion of Variance 4.035135e-06 6.929872e-08 4.119431e-08 2.059790e-08
## Cumulative Proportion 9.999999e-01 9.999999e-01 1.000000e+00 1.000000e+00
## Comp.10
## Standard deviation 0
## Proportion of Variance 0
## Cumulative Proportion 1climate.pca_avgs$loadings[, 1:2] #PC1 has high positive loading values for tmn, tmx, cwd## Comp.1 Comp.2
## ann_tmean_avg 0.36735579 0.2701106
## mean_diurnal_range_avg 0.25344989 -0.3689642
## temp_seasonality_avg 0.09229607 -0.3107929
## temp_ann_range_avg 0.19244417 -0.5407832
## tmean_wettest_quarter_avg 0.35679039 0.3009518
## tmean_driest_quarter_avg 0.35650688 0.2761101
## ann_ppt_avg -0.34490217 0.2689878
## ppt_seasonality_avg 0.34451126 0.1037666
## ppt_warmest_quarter_avg -0.39530741 -0.2143358
## ppt_coldest_quarter_avg -0.32703142 0.3255710fviz_eig(climate.pca_avgs) #scree plot 
fviz_pca_var(climate.pca_avgs, col.var = "black") #biplot
#The goal of the third visualization is to determine how much each variable is represented in a given component. Such a quality of representation is called the Cos2 and corresponds to the square cosine, and it is computed using the fviz_cos2 function. (https://www.datacamp.com/tutorial/pca-analysis-r)
#A low value means that the variable is not perfectly represented by that component.
#A high value, on the other hand, means a good representation of the variable on that component.
fviz_cos2(climate.pca_avgs, choice = "var", axes = 1:2) #axes says which PCs to use 
#biplot combined wiht cos2
file_path= "../output/Climate/RecentClim_BioClim_PCA_Avgs.png"
png(width = 5.5, height = 5, res= 300, units = "in", file=file_path, type = "cairo")
fviz_pca_var(climate.pca_avgs, col.var = "cos2",
gradient.cols = c("black", "orange", "green"),
repel = TRUE)
dev.off()## quartz_off_screen
## 2Overlay populations on PCA plot - Bioclim Recent
pc_avgs = prcomp(climate_normalized_avgs, scale = TRUE)
pc_data = data.frame(pc_avgs$x)
locs_pc = cbind(bioclim_recent_avgs, pc_data)
names(locs_pc)## [1] "parent.pop" "elevation.group"
## [3] "elev_m" "ann_tmean_avg"
## [5] "mean_diurnal_range_avg" "temp_seasonality_avg"
## [7] "temp_ann_range_avg" "tmean_wettest_quarter_avg"
## [9] "tmean_driest_quarter_avg" "ann_ppt_avg"
## [11] "ppt_seasonality_avg" "ppt_warmest_quarter_avg"
## [13] "ppt_coldest_quarter_avg" "ann_tmean_sem"
## [15] "mean_diurnal_range_sem" "temp_seasonality_sem"
## [17] "temp_ann_range_sem" "tmean_wettest_quarter_sem"
## [19] "tmean_driest_quarter_sem" "ann_ppt_sem"
## [21] "ppt_seasonality_sem" "ppt_warmest_quarter_sem"
## [23] "ppt_coldest_quarter_sem" "PC1"
## [25] "PC2" "PC3"
## [27] "PC4" "PC5"
## [29] "PC6" "PC7"
## [31] "PC8" "PC9"
## [33] "PC10"loadings = data.frame(varnames=rownames(pc_avgs$rotation), pc_avgs$rotation)
ggplot() +
geom_point(data = filter(locs_pc, elevation.group == "High"), size=2, aes(x = PC1, y = PC2), color = "#0043F0") +
geom_point(data = filter(locs_pc, elevation.group == "Mid"), size=2, aes(x = PC1, y = PC2), color = "#C9727F") +
geom_point(data = filter(locs_pc, elevation.group == "Low"), size=2, aes(x = PC1, y = PC2), color = "#F5A540") +
geom_label_repel(data = locs_pc, aes(x = PC1, y = PC2, label = parent.pop),
min.segment.length = 0, box.padding = 0.5) +
geom_vline(xintercept = 0, linetype="dashed") + geom_hline(yintercept = 0, linetype="dashed") +
theme_classic()
ggsave("../output/Climate/RecentClim_BioClimPCA_Elev_Avgs.png", width = 6, height = 4, units = "in")